Datasets:

allenai

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SciRIFF

like 32

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: would be chunked as follows (Tjong Kim Sang and Buchholz, 2000): [NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP For example, the sentence “He reckons the current account deficit will narrow to only $ 1.8 billion in September .” A lot of recent work on shallow parsing has been influenced by Abney’s work (Abney, 1991), who has suggested to “chunk” sentences to base level phrases. Citation Sentence: would be chunked as follows ( Tjong Kim Sang and Buchholz , 2000 ) : [ NP He ] [ VP reckons ] [ NP the current account deficit ] [ VP will narrow ] [ PP Context after the citation: ✁This research is supported by NSF grants IIS-9801638, ITR-IIS-0085836 and an ONR MURI Award. to ] [NP only $ 1.8 billion ] [PP in ] [NP September] . While earlier work in this direction concentrated on manual construction of rules, most of the recent work has been motivated by the observation that shallow syntactic information can be extracted using local information by examining the pattern itself, its nearby context and the local part-of-speech information. Thus, over the past few years, along with advances in the use of learning and statistical methods for acquisition of full parsers (Collins, 1997; Charniak, 1997a; Charniak, 1997b; Ratnaparkhi, 1997), significant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship (Church, 1988; Ramshaw and Marcus, 1995; Argamon et al., 1998; Cardie and Pierce, 1998; Munoz et al., 1999; Punyakanok and Roth, 2001; Buchholz et al., 1999; Tjong Kim Sang and Buchholz, 2000).

Background

acl_arc_intent_classification:train:1538

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: The flexible architecture we have presented enables interesting future research: (i) a straightforward improvement is the use of lexical similarity to reduce data sparseness, e.g. (Basili et al., 2005; Basili et al., 2006; Bloehdorn et al., 2006). It is possible that this could be improved with a better strategy for representing dependency structure for tree kernels, or if the tree kernels could be incorporated into the structural learning framework. We were not able to achieve the same performance using tree kernels as with manually extracted features. Citation Sentence: The flexible architecture we have presented enables interesting future research : ( i ) a straightforward improvement is the use of lexical similarity to reduce data sparseness , e.g. ( Basili et al. , 2005 ; Basili et al. , 2006 ; Bloehdorn et al. , 2006 ) . Context after the citation: However, the similarity between subjective words, which have multiple senses against other words may negatively impact the system accuracy. Therefore, the use of the syntactic/semantic kernels, i.e. (Bloehdorn and Moschitti, 2007a; Bloehdorn and Moschitti, 2007b), to syntactically contextualize word similarities may improve the reranker accuracy. (ii) The latter can be further boosted by studying complex structural kernels, e.g. (Moschitti, 2008; Nguyen et al., 2009; Dinarelli et al., 2009). (iii) More specific predicate argument structures such those proposed in FrameNet, e.g. (Baker et al., 1998; Giuglea and Moschitti, 2004; Giuglea and Moschitti, 2006; Johansson and Nugues, 2008b) may be useful to characterize the opinion holder and the sentence semantic context.

Background

acl_arc_intent_classification:train:1539

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Finite state transducers, which can be learned from bilingual corpora, have been proposed for automatic translation (Amengual et al., 2000), as have been bilingual stochastic grammars (Wu, 1996). This approach, known as translation memory, example-based or case-based translation, can work on the word level as well as on structured examples as they are generated during analysis and generation in more grammar-based translation paradigms (Kitano, 1993; Brown, 1996). In the simplest form, translations are stored and reused for the translation of new input. Citation Sentence: Finite state transducers , which can be learned from bilingual corpora , have been proposed for automatic translation ( Amengual et al. , 2000 ) , as have been bilingual stochastic grammars ( Wu , 1996 ) . Context after the citation: Statistical approaches (Wang and Waibel, 1997; Och et al., 1999) also fall into the category of corpus based approaches. In this paper, a translation method is proposed which is based on the very same principles as the aforementioned approaches. One difference is, that not a fully automatic training of the translation model is performed. Rather, a number of special purpose transducers are hand-crafted and used then at two points.

Background

acl_arc_intent_classification:train:154

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: 5An alternative strategy to step (4) is to perform a database lookup based on the ambiguous query and summarize the results (Litman et al., 1998), which we leave for future work. Since task initiative models contribution to domain/problemsolving goals, while dialogue initiative affects the cur- Thus, a cooperative system may adopt different strategies to achieve the same goal depending on the initiative distribution. Citation Sentence: 5An alternative strategy to step ( 4 ) is to perform a database lookup based on the ambiguous query and summarize the results ( Litman et al. , 1998 ) , which we leave for future work . Context after the citation: rent discourse goal, we developed alternative strategies for achieving the goals in Figure 4 based on initiative distribution, as shown in Table 1. The strategies employed when MIMIC has only dialogue initiative are similar to the mixed initiative dialogue strategies employed by many existing spoken dialogue systems (e.g., (Bennacef et at., 1996; Stent et al., 1999)). To instantiate an attribute, MIMIC adopts the InfoSeek dialogue act to solicit the missing information. In contrast, when MIMIC has both initiatives, it plays a more active role by presenting the user with additional information comprising valid instantiations of the attribute (GiveOptions).

FutureWork

acl_arc_intent_classification:train:1540

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Such approaches have been tried recently in restricted cases (McCallum et al., 2000; Eisner, 2001b; Lafferty et al., 2001). The resulting machine must be normalized, either per-state or globally, to obtain a joint or a conditional distribution as desired. This allows meaningful parameter tying: if certain arcs such asu:i �—*, �—*, and a:ae o:e �—* share a contextual “vowel-fronting” feature, then their weights rise and fall together with the strength of that feature. Citation Sentence: Such approaches have been tried recently in restricted cases ( McCallum et al. , 2000 ; Eisner , 2001b ; Lafferty et al. , 2001 ) . Context after the citation: Normalization may be postponed and applied instead to the result of combining the FST with other FSTs by composition, union, concatenation, etc. A simple example is a probabilistic FSA defined by normalizing the intersection of other probabilistic FSAs f1, f2,. . .. (This is in fact a log-linear model in which the component FSAs define the features: string x has log fi(x) occurrences of feature i.) In short, weighted finite-state operators provide a language for specifying a wide variety of parameterized statistical models. Let us turn to their training.

Background

acl_arc_intent_classification:train:1541

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Typed feature grammars can be used as the basis for implementations of Head-driven Phrase Structure Grammar (HPSG; Pollard and Sag, 1994) as discussed in (Gotz and Meurers, 1997a) and (Meurers and Minnen, 1997). In this paper we investigate the selective application of magic to typed feature grammars a type of constraint-logic grammar based on Typed Feature Logic (T r; G6tz, 1995). magic is an interesting technique with respect to natural language processing as it incorporates filtering into the logic underlying the grammar and enables elegant control independent filtering improvements. Citation Sentence: Typed feature grammars can be used as the basis for implementations of Head-driven Phrase Structure Grammar ( HPSG ; Pollard and Sag , 1994 ) as discussed in ( Gotz and Meurers , 1997a ) and ( Meurers and Minnen , 1997 ) . Context after the citation: Typed feature grammar constraints that are inexpensive to resolve are dealt with using the top-down interpreter of the ConTroll grammar development system (GOtz and Meurers, 1997b) which uses an advanced search function, an advanced selection function and incorporates a coroutining mechanism which supports delayed interpretation. The proposed parser is related to the so-called Lemma Table deduction system (Johnson and D6rre, 1995) which allows the user to specify whether top-down sub-computations are to be tabled. In contrast to Johnson and D6rre's deduction system, though, the selective magic parsing approach combines top-down and bottom-up control strategies. As such it resembles the parser of the grammar development system Attribute Language Engine (ALE) of (Carpenter and Penn, 1994).

Extends

acl_arc_intent_classification:train:1542

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: It is therefore no surprise that early attempts at response automation were knowledge-driven (Barr and Tessler 1995; Watson 1997; Delic and Lahaix 1998). circumstances can make each inquiry unique, and hence care must be taken to compose a response that does not confuse, irritate, or mislead the customer. 1 http://customercare.telephonyonline.com/ar/telecom next generation customer. Citation Sentence: It is therefore no surprise that early attempts at response automation were knowledge-driven ( Barr and Tessler 1995 ; Watson 1997 ; Delic and Lahaix 1998 ) . Context after the citation: These systems were carefully designed to produce relevant and correct responses, but required significant human input and maintenance (Delic and Lahaix 1998). In recent times, such knowledge-intensive approaches to content delivery have been largely superseded by data-intensive, statistical approaches. An outcome of the recent proliferation of statistical approaches, in particular in recommender systems and search engines, is that people have become accustomed to responses that are not precisely tailored to their queries. This indicates that help-desk customers may have also become more tolerant of inaccurate or incomplete automatically generated replies, provided these replies are still relevant to their problem, and so long as the customers can follow up with a request for human-generated responses if necessary.

Background

acl_arc_intent_classification:train:1543

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: A more subtle example is weighted FSAs that approximate PCFGs (Nederhof, 2000; Mohri and Nederhof, 2001), or to extend the idea, weighted FSTs that approximate joint or conditional synchronous PCFGs built for translation. Arbitrary weights such as 2.7 may be assigned to arcs or sprinkled through a regexp (to be compiled into E:E/2.7 −)arcs). P(v, z) def = Ew,x,y P(v|w)P(w, x)P(y|x)P(z|y), implemented by composing 4 machines.6,7 There are also procedures for defining weighted FSTs that are not probabilistic (Berstel and Reutenauer, 1988). Citation Sentence: A more subtle example is weighted FSAs that approximate PCFGs ( Nederhof , 2000 ; Mohri and Nederhof , 2001 ) , or to extend the idea , weighted FSTs that approximate joint or conditional synchronous PCFGs built for translation . Context after the citation: These are parameterized by the PCFG’s parameters, but add or remove strings of the PCFG to leave an improper probability distribution. Fortunately for those techniques, an FST with positive arc weights can be normalized to make it jointly or conditionally probabilistic: • An easy approach is to normalize the options at each state to make the FST Markovian. Unfortunately, the result may differ for equivalent FSTs that express the same weighted relation.

Background

acl_arc_intent_classification:train:1544

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: ones, DIRT (Lin and Pantel, 2001), VerbOcean (Chklovski and Pantel, 2004), FrameNet (Baker et al., 1998), and Wikipedia (Mehdad et al., 2010; Kouylekov et al., 2009). These include, just to mention the most popular Besides WordNet, the RTE literature documents the use of a variety of lexical information sources (Bentivogli et al., 2010; Dagan et al., 2009). Citation Sentence: ones , DIRT ( Lin and Pantel , 2001 ) , VerbOcean ( Chklovski and Pantel , 2004 ) , FrameNet ( Baker et al. , 1998 ) , and Wikipedia ( Mehdad et al. , 2010 ; Kouylekov et al. , 2009 ) . Context after the citation: DIRT is a collection of statistically learned inference rules, that is often integrated as a source of lexical paraphrases and entailment rules. VerbOcean is a graph of fine-grained semantic relations between verbs, which are frequently used as a source of precise entailment rules between predicates. FrameNet is a knowledge-base of frames describing prototypical situations, and the role of the participants they involve. It can be used as an alternative source of entailment rules, or to determine the semantic overlap between texts and hypotheses.

Background

acl_arc_intent_classification:train:1545

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The Ruby on Rails (2006) framework permits us to quickly develop web applications without rewriting common functions and classes. This allows to treat data and metadata on different level of abstraction. Messages, data and metadata are exchanged between layers using the capability of this language. Citation Sentence: The Ruby on Rails ( 2006 ) framework permits us to quickly develop web applications without rewriting common functions and classes . Context after the citation: We used the Asyncronous Javascript And XML (or AJAX) paradigm to create the graphical user interface. AJAX function lets the communication works asyncronously between a client and a server through a set of messages based on HTTP protocol and XML (Garrett, 2005).

Uses

acl_arc_intent_classification:train:1546

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: We further add rules for combining with punctuation to the left and right and allow for the merge rule X → X X of Clark and Curran (2007). We follow Lewis and Steedman (2014) in allowing a small set of generic, linguistically-plausible unary and binary grammar rules. A category (s\np)/pp might describe an intransitive verb with a prepositional phrase complement; it combines on the right (/) with a constituent with category pp, and then on the left (\) with a noun phrase (np) that serves as its subject. Citation Sentence: We further add rules for combining with punctuation to the left and right and allow for the merge rule X → X X of Clark and Curran ( 2007 ) . Context after the citation:

Uses

acl_arc_intent_classification:train:1547

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Politically-oriented text Sentiment analysis has specifically been proposed as a key enabling technology in eRulemaking, allowing the automatic analysis of the opinions that people submit (Shulman et al., 2005; Cardie et al., 2006; Kwon et al., 2006). Citation Sentence: Politically-oriented text Sentiment analysis has specifically been proposed as a key enabling technology in eRulemaking , allowing the automatic analysis of the opinions that people submit ( Shulman et al. , 2005 ; Cardie et al. , 2006 ; Kwon et al. , 2006 ) . Context after the citation: There has also been work focused upon determining the political leaning (e.g., “liberal” vs. “conservative”) of a document or author, where most previously-proposed methods make no direct use of relationships between the documents to be classified (the “unlabeled” texts) (Laver et al., 2003; Efron, 2004; Mullen and Malouf, 2006). An exception is Grefenstette et al. (2004), who experimented with determining the political orientation of websites essentially by classifying the concatenation of all the documents found on that site. Others have applied the NLP technologies of near-duplicate detection and topic-based text categorization to politically oriented text (Yang and Callan, 2005; Purpura and Hillard, 2006). Detecting agreement We used a simple method to learn to identify cross-speaker references indicating agreement.

Background

acl_arc_intent_classification:train:1548

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Moschitti et al. (2005) has made some preliminary attempt on the idea of hierarchical semantic After the PropBank (Xue and Palmer 2003) was built, Xue and Palmer (2005) and Xue (2008) have produced more complete and systematic research on Chinese SRL. This paper made the first attempt on Chinese SRL and produced promising results. Citation Sentence: Moschitti et al. ( 2005 ) has made some preliminary attempt on the idea of hierarchical semantic Context after the citation: role labeling. However, without considerations on how to utilize the characteristics of linguistically similar semantic roles, the purpose of the hierarchical system is to simplify the classification process to make it less time consuming. So the hierarchical system in their paper performs a little worse than the traditional SRL systems, although it is more efficient. Xue and Palmer (2004) did very encouraging work on the feature calibration of semantic role labeling.

Background

acl_arc_intent_classification:train:1549

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: This is noticeable for German (Brants et al., 2002) and Portuguese (Afonso et al., 2002), which still have high overall accuracy thanks to very high attachment scores, but much more conspicuous for Czech (B¨ohmov´a et al., 2003), Dutch (van der Beek et al., 2002) and Slovene (Dˇzeroski et al., 2006), where root precision drops more drastically to about 69%, 71% and 41%, respectively, and root recall is also affected negatively. A second observation is that a high proportion of non-projective structures leads to fragmentation in the parser output, reflected in lower precision for roots. Japanese (Kawata and Bartels, 2000), despite a very high accuracy, is different in that attachment score drops from 98% to 85%, as we go from length 1 to 2, which may have something to do with the data consisting of transcribed speech with very short utterances. Citation Sentence: This is noticeable for German ( Brants et al. , 2002 ) and Portuguese ( Afonso et al. , 2002 ) , which still have high overall accuracy thanks to very high attachment scores , but much more conspicuous for Czech ( B ¨ ohmov ´ a et al. , 2003 ) , Dutch ( van der Beek et al. , 2002 ) and Slovene ( Dˇzeroski et al. , 2006 ) , where root precision drops more drastically to about 69 % , 71 % and 41 % , respectively , and root recall is also affected negatively . Context after the citation: On the other hand, all three languages behave like high-accuracy languages with respect to attachment score. A very similar pattern is found for Spanish (Civit Torruella and MartiAntonin, 2002), although this cannot be explained by a high proportion of non-projective structures. One possible explanation in this case may be the fact that dependency graphs in the Spanish data are sparsely labeled, which may cause problem for a parser that relies on dependency labels as features. The results for Arabic (Hajiˇc et al., 2004; Smrˇz et al., 2002) are characterized by low root accuracy

CompareOrContrast

acl_arc_intent_classification:train:155

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Morris and Hirst (2004) pointed out that many relations between words in a text are non-classical (i.e. other than typical taxonomic relations like synonymy or hypernymy) and therefore not covered by semantic similarity. Automatic corpus-based selection of word pairs is more objective, leading to a balanced dataset with pairs connected by all kinds of lexical-semantic relations. Furthermore, manually selected word pairs are often biased towards highly related pairs (Gurevych, 2006), because human annotators tend to select only highly related pairs connected by relations they are aware of. Citation Sentence: Morris and Hirst ( 2004 ) pointed out that many relations between words in a text are non-classical ( i.e. other than typical taxonomic relations like synonymy or hypernymy ) and therefore not covered by semantic similarity . Context after the citation: Previous studies only considered semantic relatedness (or similarity) of words rather than concepts. However, polysemous or homonymous words should be annotated on the level of concepts. If we assume that bank has two meanings (“financial institution” vs. “river bank”)5 and it is paired with money, the result is two sense quali- 5WordNet lists 10 meanings.

Background

acl_arc_intent_classification:train:1550

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Some of the intuitions we associate with this notion have been very well expressed by Turner (1987, pp. 7-8): ... Semantics is constrained by our models of ourselves and our worlds. The referential structures we are going to use are collections of logical theories, but the concept of reference is more general. This means that information contained in grammars and dictionaries can be used to constrain possible interpretations of the logical predicates of an object-level theory. Citation Sentence: Some of the intuitions we associate with this notion have been very well expressed by Turner ( 1987 , pp. 7-8 ) : ... Semantics is constrained by our models of ourselves and our worlds . Context after the citation: We have models of up and down that are based by the way our bodies actually function. Once the word "up" is given its meaning relative to our experience with gravity, it is not free to "slip" into its opposite. "Up" means up and not down. ... We have a model that men and women couple to produce offspring who are similar to their parents, and this model is grounded in genetics, and the semantics of kinship metaphor is grounded in this model.

Background

acl_arc_intent_classification:train:1551

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: In 2009, the second WePS campaign showed similar trends regarding the use of NE features (Artiles et al., 2009). Other features used by the systems include noun phrases (Chen and Martin, 2007), word n-grams (Popescu and Magnini, 2007), emails and URLs (del ValleAgudo et al., 2007), etc. This makes NEs the second most common type of feature; only the BoW feature was more popular. Citation Sentence: In 2009 , the second WePS campaign showed similar trends regarding the use of NE features ( Artiles et al. , 2009 ) . Context after the citation: Due to the complexity of systems, the results of the WePS evaluation do not provide a direct answer regarding the advantages of using NEs over other computationally lighter features such as BoW or word n-grams. But the WePS campaigns did provide a useful, standardised resource to perform the type of studies that were not possible before. In the next Section we describe this dataset and how it has been adapted for our purposes. 1By team ID: CU-COMSEM, IRST-BP, PSNUS, SHEF, FICO, UNN, AUG, JHU1, DFKI2, UC3M13

Background

acl_arc_intent_classification:train:1552

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The head words can be automatically extracted using a heuristic table lookup in the manner described by Magerman (1994). For example, (wrote a book in three days, attach-verb) would be annotated as (wrote, book, in, days, verb). preposition, and the prepositional noun phrase, respectively, and a specifies the attachment classification. Citation Sentence: The head words can be automatically extracted using a heuristic table lookup in the manner described by Magerman ( 1994 ) . Context after the citation: For this learning problem, the supervision is the one-bit information of whether p should attach to v or to n. In order to learn the attachment preferences of prepositional phrases, the system builds attachment statistics for each the characteristic tuple of all training examples. A characteristic tuple is some subset of the four head words in the example, with the condition that one of the elements must be the preposition. Each training example forms eight characteristic tuples: (v, n, p, n2), (v, n, p), (v, p, n2), (n, p, n2), (v, p), (n, p), (p, n2), (p).

Uses

acl_arc_intent_classification:train:1553

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Reiter describes a pipelined modular approach as a consensus architecture underlying most recent work in generation (Reiter 1994). McDonald has even argued for extending the model to a large number of components (McDonald 1988), and several systems have indeed added an additional component between the planner and the linguistic component (Meteer 1994; Panaget 1994; Wanner 1994). For example, DIOGENES (Nirenburg et al. 1988), EPICURE (Dale 1989), SPOKESMAN (Meteer 1989), Sibun's work on local organization of text (Sibun 1991), and COMET (Fisher and McKeown 1990) all are organized this way. Citation Sentence: Reiter describes a pipelined modular approach as a consensus architecture underlying most recent work in generation ( Reiter 1994 ) . Context after the citation: As this large body of work makes clear, the modular approach has been very useful, simplifying the design of generators and making them more flexible. In fact, in at least one case the "tactical" component of a generator was successfully replaced with a radically different independently designed one (Rubinoff 1986). A modular design, 1 Danlos uses "syntactic" rather than "tactical"; see the note on page 122 of Danlos (1987).

Background

acl_arc_intent_classification:train:1554

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: For example, the suite of LT tools (Mikheev et al., 1999; Grover et al., 2000) perform tokenization, tagging and chunking on XML marked-up text directly. A number of stand-alone tools have also been developed. This allows components to be highly configurable and simplifies the addition of new components to the system. Citation Sentence: For example , the suite of LT tools ( Mikheev et al. , 1999 ; Grover et al. , 2000 ) perform tokenization , tagging and chunking on XML marked-up text directly . Context after the citation: These tools also store their configuration state, e.g. the transduction rules used in LT CHUNK, in XML configuration files. This gives a greater flexibility but the tradeoff is that these tools can run very slowly. Other tools have been designed around particular techniques, such as finite state machines (Karttunen et al., 1997; Mohri et al., 1998). However, the source code for these tools is not freely available, so they cannot be extended.

Background

acl_arc_intent_classification:train:1555

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Liu et al. (2005), Meral et al. (2007), Murphy (2001), Murphy and Vogel (2007) and Topkara et al. (2006a) all belong to the syntactic transformation category. In other words, instead of performing lexical substitution directly to the text, the secret message is embedded into syntactic parse trees of the sentences. Later, Atallah et al. (2001b) embedded information in the tree structure of the text by adjusting the structural properties of intermediate representations of sentences. Citation Sentence: Liu et al. ( 2005 ) , Meral et al. ( 2007 ) , Murphy ( 2001 ) , Murphy and Vogel ( 2007 ) and Topkara et al. ( 2006a ) all belong to the syntactic transformation category . Context after the citation: After embedding the secret message, modified deep structure forms are converted into the surface structure format via language generation tools. Atallah et al. (2001b) and Topkara et al. (2006a) attained the embedding capacity of 0.5 bits per sentence with the syntactic transformation method.

Background

acl_arc_intent_classification:train:1556

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: Brockmann and Lapata (2003) have showed that WordNet-based approaches do not always outperform simple frequency-based models, and a number of techniques have been recently proposed which may offer ideas for refining our current unsupervised approach (Erk, 2007; Bergsma et al., 2008). Considerable research has been done on SP acquisition most of which has involved collecting argument headwords from data and generalizing to WordNet classes. In addition to the ideas mentioned earlier, our future plans include looking into optimal ways of acquiring SPs for verb classification. Citation Sentence: Brockmann and Lapata ( 2003 ) have showed that WordNet-based approaches do not always outperform simple frequency-based models , and a number of techniques have been recently proposed which may offer ideas for refining our current unsupervised approach ( Erk , 2007 ; Bergsma et al. , 2008 ) . Context after the citation: The number and type (and combination) of GRs for which SPs can be reliably acquired, especially when the data is sparse, requires also further investigation. In addition, we plan to investigate other potentially useful features for verb classification (e.g. named entities and preposition classes) and explore semi-automatic ML technology and active learning for guiding the classification. Finally, we plan to conduct a bigger experiment with a larger number of verbs, and conduct evaluation in the context of practical application tasks.

FutureWork

acl_arc_intent_classification:train:1557

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The RenTAL system is implemented in LiLFeS (Makino et al., 1998)2. Citation Sentence: The RenTAL system is implemented in LiLFeS ( Makino et al. , 1998 ) 2 . Context after the citation: LiLFeS is one of the fastest inference engines for processing feature structure logic, and efficient HPSG parsers have already been built on this system (Nishida et al., 1999; Torisawa et al., 2000). We applied our system to the XTAG English grammar (The XTAG Research Group, 2001)3, which is a large-scale FB-LTAG grammar for English. 2The RenTAL system is available at: http://www-tsujii.is.s.u-tokyo.ac.jp/rental/ 3We used the grammar attached to the latest distribution of an LTAG parser which we used for the parsing experiment. The parser is available at: ftp://ftp.cis.upenn.edu/pub/xtag/lem/lem-0.13.0.i686.tgz

Uses

acl_arc_intent_classification:train:1558

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: In the future, we hope to evaluate the automatic annotations and extracted lexicon against Propbank (Kingsbury and Palmer 2002). Our error analysis also revealed some interesting issues associated with using an external standard such as COMLEX. We believe our semantic forms are fine-grained, and by choosing to evaluate against COMLEX, we set our sights high: COMLEX is considerably more detailed than the OALD or LDOCE used for other earlier evaluations. Citation Sentence: In the future , we hope to evaluate the automatic annotations and extracted lexicon against Propbank ( Kingsbury and Palmer 2002 ) . Context after the citation: Apart from the related approach of Miyao, Ninomiya, and Tsujii (2004), which does not distinguish between argument and adjunct prepositional phrases, our treebank and automatic f-structure annotation-based architecture for the automatic acquisition of detailed subcategorization frames is quite unlike any of the architectures presented in the literature. Subcategorization frames are reverse-engineered and almost a byproduct of the automatic f-structure annotation algorithm. It is important to realize that the induction of lexical resources is part of a larger project on the acquisition of wide-coverage, robust, probabilistic, deep unification grammar resources from treebanks Burke, Cahill, et al. (2004b). We are already using the extracted semantic forms in parsing new text with robust, wide-coverage probabilistic LFG grammar approximations automatically acquired from the f-structure-annotated Penn-II treebank, specifically in the resolution of LDDs, as described in Cahill, Burke, et al. (2004).

FutureWork

acl_arc_intent_classification:train:1559

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: 3 The degree of precision of the measurement (James et al. 1996, Section 1.5) determines which objects can be described by the GRE algorithm, since it determines which objects count as having the same size. Suppose the target is c4: As a result, all other properties that turn up in the NP are already in the list L when size is added. Citation Sentence: 3 The degree of precision of the measurement ( James et al. 1996 , Section 1.5 ) determines which objects can be described by the GRE algorithm , since it determines which objects count as having the same size . Context after the citation: 4 To turn this likelihood into a certainty, one can add a test at the end of the algorithm, which adds a type-related property if none is present yet (cfXXX, Dale and Reiter 1995). VAGUE uses both of these devices. Since gradable properties are (for now at least) assumed to be dispreferred, the first property that makes it into L is ‘mouse,’ which removes p5 from the context set. (Result: C = {c1,...,c4}.)

Background

acl_arc_intent_classification:train:156

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Each set of translations is stored separately, and for each set the “marker hypothesis” (Green 1979) is used to segment the phrasal lexicon into a “marker lexicon.” The primary resource gathered is a “phrasal lexicon,” constructed by extracting over 200,000 phrases from the Penn Treebank and having them translated into French by three Web-based machine translation (MT) systems. In Section 2, we describe how we automatically obtain a hierarchy of lexical resources that are used sequentially by our EBMT system, wEBMT, to translate new input. Citation Sentence: Each set of translations is stored separately , and for each set the `` marker hypothesis '' ( Green 1979 ) is used to segment the phrasal lexicon into a `` marker lexicon . '' Context after the citation: The marker hypothesis is a universal psycholinguistic constraint which states that natural languages are ”marked” for complex syntactic structure at surface form by a closed set of specific lexemes and morphemes. That is, a basic phrase-level segmentation of an input sentence can be achieved by exploiting a closed list of known marker words to signal the start and end of each segment. Consider the following example, selected at random from the Wall Street Journal section of the Penn-II Treebank: (5) The Dearborn, Mich., energy company stopped paying a dividend in the third quarter of 1984 because of troubles at its Midland nuclear plant. Here we see that three noun phrases start with determiners and one with a possessive pronoun.

Uses

acl_arc_intent_classification:train:1560

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: One way to increase the precision of the mapping process is to impose some linguistic constraints on the sequences such as simple noun-phrase contraints (Gaussier, 1995; Kupiec, 1993; hua Chen and Chen, 94; Fung, 1995; Evans and Zhai, 1996). Citation Sentence: One way to increase the precision of the mapping process is to impose some linguistic constraints on the sequences such as simple noun-phrase contraints ( Gaussier , 1995 ; Kupiec , 1993 ; hua Chen and Chen , 94 ; Fung , 1995 ; Evans and Zhai , 1996 ) . Context after the citation: It is also possible to focus on non-compositional compounds, a key point in bilingual applications (Su et al., 1994; Melamed, 1997; Lin, 99). Another interesting approach is to restrict sequences to those that do not cross constituent boundary patterns (Wu, 1995; Furuse and Iida, 96). In this study, we filtered for potential sequences that are likely to be noun phrases, using simple regular expressions over the associated part-of-speech tags. An excerpt of the association probabilities of a unit model trained considering only the NP-sequences is given in table 3.

Uses

acl_arc_intent_classification:train:1561

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Although this study falls under the general topic of discourse modeling, our work differs from previous attempts to characterize text in terms of domainindependent rhetorical elements (McKeown, 1985; Marcu and Echihabi, 2002). Nevertheless, their work bolsters our claims regarding the usefulness of generative models in extrinsic tasks, which we do not describe here. Whereas Barzilay and Lee evaluated their work in the context of document summarization, the fourpart structure of medical abstracts allows us to conduct meaningful intrinsic evaluations and focus on the sentence classification task. Citation Sentence: Although this study falls under the general topic of discourse modeling , our work differs from previous attempts to characterize text in terms of domainindependent rhetorical elements ( McKeown , 1985 ; Marcu and Echihabi , 2002 ) . Context after the citation: Our task is closer to the work of Teufel and Moens (2000), who looked at the problem of intellectual attribution in scientific texts.

CompareOrContrast

acl_arc_intent_classification:train:1562

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: "Coherence," as outlined above, can be understood as a declarative (or static) version of marker passing (Hirst 1987; Charniak 1983), with one difference: the activation spreads to theories that share a predicate, not through the IS-A hierarchy, and is limited to elementary facts about predicates appearing in the text. The idea of using preferences among theories is new, hence it was described in more detail. Moreover, in addition to proposing this structure of R, we have described the two mechanisms for exploiting it, "coherence" and "dominance," which are not variants of the standard first order entailment, but abduction. Citation Sentence: `` Coherence , '' as outlined above , can be understood as a declarative ( or static ) version of marker passing ( Hirst 1987 ; Charniak 1983 ) , with one difference : the activation spreads to theories that share a predicate , not through the IS-A hierarchy , and is limited to elementary facts about predicates appearing in the text . Context after the citation: The metalevel rules we are going to discuss in Section 6, and that deal with the Gricean maxims and the meaning of "but," can be easily expressed in the languages of set theory or higher order logic, but not everything expressible in those languages makes sense in natural language. Hence, putting limitations on the expressive power of the language of the metalevel will remain as one of many open problems. 4. Coherence of Paragraphs

CompareOrContrast

acl_arc_intent_classification:train:1563

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Task properties Determining whether or not a speaker supports a proposal falls within the realm of sentiment analysis, an extremely active research area devoted to the computational treatment of subjective or opinion-oriented language (early work includes Wiebe and Rapaport (1988), Hearst (1992), Sack (1994), and Wiebe (1994); see Esuli (2006) for an active bibliography). Note that from an experimental point of view, this is a very convenient problem to work with because we can automatically determine ground truth (and thus avoid the need for manual annotation) simply by consulting publicly available voting records. In this paper, we investigate the following specific instantiation of this problem: we seek to determine from the transcripts of U.S. Congressional floor debates whether each “speech” (continuous single-speaker segment of text) represents support for or opposition to a proposed piece of legislation. Citation Sentence: Task properties Determining whether or not a speaker supports a proposal falls within the realm of sentiment analysis , an extremely active research area devoted to the computational treatment of subjective or opinion-oriented language ( early work includes Wiebe and Rapaport ( 1988 ) , Hearst ( 1992 ) , Sack ( 1994 ) , and Wiebe ( 1994 ) ; see Esuli ( 2006 ) for an active bibliography ) . Context after the citation: In particular, since we treat each individual speech within a debate as a single “document”, we are considering a version of document-level sentiment-polarity classification, namely, automatically distinguishing between positive and negative documents (Das and Chen, 2001; Pang et al., 2002; Turney, 2002; Dave et al., 2003). Most sentiment-polarity classifiers proposed in the recent literature categorize each document independently. A few others incorporate various measures of inter-document similarity between the texts to be labeled (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006). Many interesting opinion-oriented documents, however, can be linked through certain relationships that occur in the context of evaluative discussions.

Background

acl_arc_intent_classification:train:1564

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: First, it has been noted that in many natural language applications it is sufficient to use shallow parsing information; information such as noun phrases (NPs) and other syntactic sequences have been found useful in many large-scale language processing applications including information extraction and text summarization (Grishman, 1995; Appelt et al., 1993). Research on shallow parsing was inspired by psycholinguistics arguments (Gee and Grosjean, 1983) that suggest that in many scenarios (e.g., conversational) full parsing is not a realistic strategy for sentence processing and analysis, and was further motivated by several arguments from a natural language engineering viewpoint. Thus, over the past few years, along with advances in the use of learning and statistical methods for acquisition of full parsers (Collins, 1997; Charniak, 1997a; Charniak, 1997b; Ratnaparkhi, 1997), significant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship (Church, 1988; Ramshaw and Marcus, 1995; Argamon et al., 1998; Cardie and Pierce, 1998; Munoz et al., 1999; Punyakanok and Roth, 2001; Buchholz et al., 1999; Tjong Kim Sang and Buchholz, 2000). Citation Sentence: First , it has been noted that in many natural language applications it is sufficient to use shallow parsing information ; information such as noun phrases ( NPs ) and other syntactic sequences have been found useful in many large-scale language processing applications including information extraction and text summarization ( Grishman , 1995 ; Appelt et al. , 1993 ) . Context after the citation: Second, while training a full parser requires a collection of fully parsed sentences as training corpus, it is possible to train a shallow parser incrementally. If all that is available is a collection of sentences annotated for NPs, it can be used to produce this level of analysis. This can be augmented later if more information is available. Finally, the hope behind this research direction was that this incremental and modular processing might result in more robust parsing decisions, especially in cases of spoken language or other cases in which the quality of the natural language inputs is low sentences which may have repeated words, missing words, or any other lexical and syntactic mistakes.

Background

acl_arc_intent_classification:train:1565

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: In addition, a fully flexible access system allows the retrieval of dictionary entries on the basis of constraints specifying any combination of phonetic, lexical, syntactic, and semantic information (Boguraev et al., 1987). In addition to headwords, dictionary search through the pronunciation field is available; Carter (1987) has merged information from the pronunciation and hyphenation fields, creating an enhanced phonological representation which allows access to entries by broad phonetic class and syllable structure (Huttenlocher and Zue, 1983). From the master LDOCE file, we have computed alternative indexing information, which allows access into the dictionary via different routes. Citation Sentence: In addition , a fully flexible access system allows the retrieval of dictionary entries on the basis of constraints specifying any combination of phonetic , lexical , syntactic , and semantic information ( Boguraev et al. , 1987 ) . Context after the citation: Independently, random selection of dictionary entries is also provided to allow the testing of software on an unbiased sample.

Background

acl_arc_intent_classification:train:1566

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Clarkson and Robinson (1997) developed a way of incorporating standard n-grams into the cache model, using mixtures of language models and also exponentially decaying the weight for the cache prediction depending on the recency of the word’s last But unlike the cache model, it uses a multipass strategy. The DCA system is similar in spirit to such dynamic adaptation: it applies word n-grams collected on the fly from the document under processing and favors them more highly than the default assignment based on prebuilt lists. Citation Sentence: Clarkson and Robinson ( 1997 ) developed a way of incorporating standard n-grams into the cache model , using mixtures of language models and also exponentially decaying the weight for the cache prediction depending on the recency of the word 's last Context after the citation: occurrence. In our experiments we applied simple linear interpolation to incorporate the DCA system into a POS tagger. Instead of decaying nonlocal information, we opted for not propagating it from one document for processing of another. For handling very long documents with our method, however, the information decay strategy seems to be the right way to proceed.

Extends

acl_arc_intent_classification:train:1567

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Riehemann 1993; Oliva 1994; Frank 1994; Opalka 1995; Sanfilippo 1995). In a number of proposals, lexical generalizations are captured using lexical underspecification (Kathol 1994; Krieger and Nerbonne 1992; Lexical rules have not gone unchallenged as a mechanism for expressing generalizations over lexical information. Citation Sentence: Riehemann 1993 ; Oliva 1994 ; Frank 1994 ; Opalka 1995 ; Sanfilippo 1995 ) . Context after the citation: The lexical entries are only partially specified, and various specializations are encoded via the type hierarchy, definite clause attachments, or a macro hierarchy. These approaches seem to propose a completely different way to capture lexical generalizations. It is therefore interesting that the covariation lexical rule compiler produces a lexicon encoding that, basically, uses an underspecification representation: The resulting definite clause representation after constraint propagation represents the common information in the base lexical entry, and uses a definite clause attachment to encode the different specializations. 8.

CompareOrContrast

acl_arc_intent_classification:train:1568

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Machine learning methods should be interchangeable: Transformation-based learning (TBL) (Brill, 1993) and Memory-based learning (MBL) (Daelemans et al., 2002) have been applied to many different problems, so a single interchangeable component should be used to represent each method. This is particularly important in NLP because of the high redundancy across tasks and approaches. It also ensures components are maximally composable and extensible. Citation Sentence: Machine learning methods should be interchangeable : Transformation-based learning ( TBL ) ( Brill , 1993 ) and Memory-based learning ( MBL ) ( Daelemans et al. , 2002 ) have been applied to many different problems , so a single interchangeable component should be used to represent each method . Context after the citation: We will base these components on the design of Weka (Witten and Frank, 1999). Representations should be reusable: for example, named entity classification can be considered as a sequence tagging task or a bag-of-words text classification task. The same beam-search sequence tagging component should be able to be used for POS tagging, chunking and named entity classification. Feature extraction components should be reusable since many NLP components share features, for instance, most sequence taggers use the previously assigned tags.

Motivation

acl_arc_intent_classification:train:1569

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: There have already been several attempts to develop distributed NLP systems for dialogue systems (Bayer et al., 2001) and speech recognition (Hacioglu and Pellom, 2003). This standardisation of remote procedures is very exciting from a software engineering viewpoint since it allows systems to be totally distributed. Systems can automatically discover and communicate with web services that provide the functionality they require by querying databases of standardised descriptions of services with WSDL and UDDI. Citation Sentence: There have already been several attempts to develop distributed NLP systems for dialogue systems ( Bayer et al. , 2001 ) and speech recognition ( Hacioglu and Pellom , 2003 ) . Context after the citation: Web services will allow components developed by different researchers in different locations to be composed to build larger systems. Because web services are of great commercial interest they are already being supported strongly by many programming languages. For instance, web services can be accessed with very little code in Java, Python, Perl, C, C++ and Prolog. This allows us to provide NLP services to many systems that we could not otherwise support using a single interface definition.

Background

acl_arc_intent_classification:train:157

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Recently, several alternative, often quite sophisticated approaches to collective classification have been proposed (Neville and Jensen, 2000; Lafferty et al., 2001; Getoor et al., 2002; Taskar et al., 2002; Taskar et al., 2003; Taskar et al., 2004; McCallum and Wellner, 2004). Zhu (2005) maintains a survey of this area. Notable early papers on graph-based semisupervised learning include Blum and Chawla (2001), Bansal et al. (2002), Kondor and Lafferty (2002), and Joachims (2003). Citation Sentence: Recently , several alternative , often quite sophisticated approaches to collective classification have been proposed ( Neville and Jensen , 2000 ; Lafferty et al. , 2001 ; Getoor et al. , 2002 ; Taskar et al. , 2002 ; Taskar et al. , 2003 ; Taskar et al. , 2004 ; McCallum and Wellner , 2004 ) . Context after the citation: It would be interesting to investigate the application of such methods to our problem. However, we also believe that our approach has important advantages, including conceptual simplicity and the fact that it is based on an underlying optimization problem that is provably and in practice easy to solve.

Background

acl_arc_intent_classification:train:1570

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: We tested the classification of verbs into semantic types using a verb list of 139 pre-classified items drawn from the lists published in Rosenbaum (1967) and Stockwell et al. (1973). It is not clear, in particular, that the rules for computing semantic types for verbs are well enough motivated linguistically or that the LDOCE lexicographers were sensitive enough to the different transformational potential of the various classes of verbs to make a rule such as our one for Object Raising viable. Therefore, we have undertaken a limited test of both the accuracy of the assignment of the LDOCE codes in the source dictionary and the reliability of the more ambitious (and potentially controversial) aspects of the grammar code transformation rules. Citation Sentence: We tested the classification of verbs into semantic types using a verb list of 139 pre-classified items drawn from the lists published in Rosenbaum ( 1967 ) and Stockwell et al. ( 1973 ) . Context after the citation: Figure 16 gives the number of verbs classified under each category by these authors and the number successfully classified into the same categories by the system. The overall error rate of the system was 14%; however, as the table illustrates, the rules discussed above classify verbs into Subject Raising, Subject Equi and persuade v 1 [Ti (of); D5] to cause to feel certain-i CONVINCE: She was not persuaded of the ttllth of his statement 2 [T1(into, out of); V3] to cause to do something by reasoning, arguing, begging, etc.: try to persuade him to let us go with him. I Nothing would persuade him. Object Equi very successfully.

Uses

acl_arc_intent_classification:train:1571

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The combination of likelihood and prior modeling, HMMs, and Viterbi decoding is fundamentally the same as the standard probabilistic approaches to speech recognition (Bahl, Jelinek, and Mercer 1983) and tagging (Church 1988). Dialogue Act Modeling sequence with the highest posterior probability: Stolcke et al. Citation Sentence: The combination of likelihood and prior modeling , HMMs , and Viterbi decoding is fundamentally the same as the standard probabilistic approaches to speech recognition ( Bahl , Jelinek , and Mercer 1983 ) and tagging ( Church 1988 ) . Context after the citation: It maximizes the probability of getting the entire DA sequence correct, but it does not necessarily find the DA sequence that has the most DA labels correct (Dermatas and Kokkinakis 1995). To minimize the total number of utterance labeling errors, we need to maximize the probability of getting each DA label correct individually, i.e., we need to maximize P(U11E) for each i = 1,. . . ,n. We can compute the per-utterance posterior DA probabilities by summing: where the summation is over all sequences U whose ith element matches the label in question.

CompareOrContrast

acl_arc_intent_classification:train:1572

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Cross-lingual Textual Entailment (CLTE) has been proposed by (Mehdad et al., 2010) as an extension of Textual Entailment (Dagan and Glickman, 2004) that consists in deciding, given two texts T and H in different languages, if the meaning of H can be inferred from the meaning of T. Citation Sentence: Cross-lingual Textual Entailment ( CLTE ) has been proposed by ( Mehdad et al. , 2010 ) as an extension of Textual Entailment ( Dagan and Glickman , 2004 ) that consists in deciding , given two texts T and H in different languages , if the meaning of H can be inferred from the meaning of T . Context after the citation: The task is inherently difficult, as it adds issues related to the multilingual dimension to the complexity of semantic inference at the textual level. For instance, the reliance of current monolingual TE systems on lexical resources (e.g. WordNet, VerbOcean, FrameNet) and deep processing components (e.g. syntactic and semantic parsers, co-reference resolution tools, temporal expressions recognizers and normalizers) has to confront, at the cross-lingual level, with the limited availability of lexical/semantic resources covering multiple languages, the limited coverage of the existing ones, and the burden of integrating languagespecific components into the same cross-lingual architecture. As a first step to overcome these problems, (Mehdad et al., 2010) proposes a “basic solution”, that brings CLTE back to the monolingual scenario by translating H into the language of T. Despite the advantages in terms of modularity and portability of the architecture, and the promising experimental results, this approach suffers from one main limitation which motivates the investigation on alternative solutions. Decoupling machine translation (MT) and TE, in fact, ties CLTE performance to the availability of MT components, and to the quality of the translations.

Background

acl_arc_intent_classification:train:1573

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The EDR has close ties to the named entity recognition (NER) and coreference resolution tasks, which have been the focus of several recent investigations (Bikel et al., 1997; Miller et al., 1998; Borthwick, 1999; Mikheev et al., 1999; Soon et al., 2001; Ng and Cardie, 2002; Florian et al., 2004), and have been at the center of evaluations such as: MUC-6, MUC-7, and the CoNLL'02 and CoNLL'03 shared tasks. In this paper we focus on the Entity Detection and Recognition task (EDR) for Arabic as described in ACE 2004 framework (ACE, 2004). These tasks have applications in summarization, information retrieval (one can get all hits for Washington/person and not the ones for Washington/state or Washington/city), data mining, question answering, language understanding, etc. Citation Sentence: The EDR has close ties to the named entity recognition ( NER ) and coreference resolution tasks , which have been the focus of several recent investigations ( Bikel et al. , 1997 ; Miller et al. , 1998 ; Borthwick , 1999 ; Mikheev et al. , 1999 ; Soon et al. , 2001 ; Ng and Cardie , 2002 ; Florian et al. , 2004 ) , and have been at the center of evaluations such as : MUC-6 , MUC-7 , and the CoNLL '02 and CoNLL '03 shared tasks . Context after the citation: Usually, in computational linguistics literature, a named entity is an instance of a location, a person, or an organization, and the NER task consists of identifying each of these occurrences. Instead, we will adopt the nomenclature of the Automatic Content Extraction program (NIST, 2004): we will call the instances of textual references to objects/abstractions mentions, which can be either named (e.g. John Mayor), nominal (the president) or pronominal (she, it). An entity is the aggregate of all the mentions (of any level) which refer to one conceptual entity. For instance, in the sentence

Background

acl_arc_intent_classification:train:1574

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Other solutions such as complete caching of the corpora are not typically adopted due to legal concerns over copyright and redistribution of web data, issues considered at length by Fletcher (2004a). Current practise elsewhere includes the distribution of URL lists, but given the dynamic nature of the web, this is not sufficiently robust. We will also investigate whether the distributed environment underlying our approach offers a solution to the problem of reproducibility in web-based corpus studies based in general. Citation Sentence: Other solutions such as complete caching of the corpora are not typically adopted due to legal concerns over copyright and redistribution of web data , issues considered at length by Fletcher ( 2004a ) . Context after the citation: Other requirements for reference corpora such as retrieval and storage of metadata for web pages are beyond the scope of what we propose here. To improve the representative nature of webderived corpora, we will research techniques to enable the importing of additional document types such as PDF. We will reuse and extend techniques implemented in the collection, encoding and annotation of the PERC Corpus of Professional English12. A majority of this corpus has been collected by conversion of on-line academic journal articles from PDF to XML with a combination of semi-automatic tools and techniques (including Adobe Acrobat version 6).

Background

acl_arc_intent_classification:train:1575

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This includes work on generalized expectation (Mann and McCallum, 2010), posterior regularization (Ganchev et al., 2010) and constraint driven learning (Chang et al., 2007; Chang et al., 2010). There have been a number of efforts to exploit weak or external signals of quality to train better prediction models. We call our algorithm augmented-loss training as it optimizes multiple losses to augment the traditional supervised parser loss. Citation Sentence: This includes work on generalized expectation ( Mann and McCallum , 2010 ) , posterior regularization ( Ganchev et al. , 2010 ) and constraint driven learning ( Chang et al. , 2007 ; Chang et al. , 2010 ) . Context after the citation: The work of Chang et al. (2007) on constraint driven learning is perhaps the closest to our framework and we draw connections to it in Section 5. In these studies the typical goal is to use the weak signal to improve the structured prediction models on the intrinsic evaluation metrics. For our setting this would mean using weak application specific signals to improve dependency parsing. Though we explore such ideas in our experiments, in particular for semi-supervised domain adaptation, we are primarily interested in the case where the weak signal is precisely what we wish to optimize, but also desire the benefit from using both data with annotated parse structures and data specific to the task at hand to guide parser training.

Background

acl_arc_intent_classification:train:1576

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: A very similar formulation, for another grammar transformation, is given in Nederhof (1998). The full formulation of the integrated grammar transformation and construction of the finite automaton is rather long and is therefore not given here. A set of such nonterminals can therefore be treated as the corresponding case from Figure 2, assuming the value right. Citation Sentence: A very similar formulation , for another grammar transformation , is given in Nederhof ( 1998 ) . Context after the citation:

CompareOrContrast

acl_arc_intent_classification:train:1577

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: We run GIZA++ (Och and Ney, 2000) on the training corpus in both directions (Koehn et al., 2003) to obtain the word alignment for each sentence pair. The development set is NIST02 evaluation data, and the test datasets are NIST05, NIST06,and NIST08. The training data is FBIS corpus consisting of about 240k sentence pairs. Citation Sentence: We run GIZA + + ( Och and Ney , 2000 ) on the training corpus in both directions ( Koehn et al. , 2003 ) to obtain the word alignment for each sentence pair . Context after the citation: We train a 4-gram language model on the Xinhua portion of the English Gigaword corpus using the SRILM Toolkits (Stolcke, 2002) with modified Kneser-Ney smoothing (Chen and Goodman, 1998). In our experiments the translation performances are measured by case-insensitive BLEU4 metric (Papineni et al., 2002) and we use mtevalv13a.pl as the evaluation tool. The significance testing is performed by paired bootstrap re-sampling (Koehn, 2004). We use an in-house developed hierarchical phrase-based translation (Chiang, 2005) as our baseline system, and we denote it as In-Hiero.

Uses

acl_arc_intent_classification:train:1578

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Other attempts to address efficiency include the fast Transformation Based Learning (TBL) Toolkit (Ngai and Florian, 2001) which dramatically speeds up training TBL systems, and the translation of TBL rules into finite state machines for very fast tagging (Roche and Schabes, 1997). An example of this is the estimation of maximum entropy models, from simple iterative estimation algorithms used by Ratnaparkhi (1998) that converge very slowly, to complex techniques from the optimisation literature that converge much more rapidly (Malouf, 2002). However, it will be increasingly important as techniques become more complex and corpus sizes grow. Citation Sentence: Other attempts to address efficiency include the fast Transformation Based Learning ( TBL ) Toolkit ( Ngai and Florian , 2001 ) which dramatically speeds up training TBL systems , and the translation of TBL rules into finite state machines for very fast tagging ( Roche and Schabes , 1997 ) . Context after the citation: The TNT POS tagger (Brants, 2000) has also been designed to train and run very quickly, tagging between 30,000 and 60,000 words per second. The Weka package (Witten and Frank, 1999) provides a common framework for several existing machine learning methods including decision trees and support vector machines. This library has been very popular because it allows researchers to experiment with different methods without having to modify code or reformat data. Finally, the Natural Language Toolkit (NLTK) is a package of NLP components implemented in Python (Loper and Bird, 2002).

Background

acl_arc_intent_classification:train:1579

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Table look-up using an explicit translation lexicon is sufficient and preferable for many multilingual NLP applications, including "crummy" MT on the World Wide Web (Church & Hovy, 1993), certain machine-assisted translation tools (e.g. (Macklovitch, 1994; Melamed, 1996b)), concordancing for bilingual lexicography (Catizone et al., 1993; Gale & Church, 1991), computerassisted language learning, corpus linguistics (Melby. However, the IBM models, which attempt to capture a broad range of translation phenomena, are computationally expensive to apply. Over the past decade, researchers at IBM have developed a series of increasingly sophisticated statistical models for machine translation (Brown et al., 1988; Brown et al., 1990; Brown et al., 1993a). Citation Sentence: Table look-up using an explicit translation lexicon is sufficient and preferable for many multilingual NLP applications , including `` crummy '' MT on the World Wide Web ( Church & Hovy , 1993 ) , certain machine-assisted translation tools ( e.g. ( Macklovitch , 1994 ; Melamed , 1996b ) ) , concordancing for bilingual lexicography ( Catizone et al. , 1993 ; Gale & Church , 1991 ) , computerassisted language learning , corpus linguistics ( Melby . Context after the citation: 1981), and cross-lingual information retrieval (Oard & Dorr, 1996). In this paper, we present a fast method for inducing accurate translation lexicons. The method assumes that words are translated one-to-one. This assumption reduces the explanatory power of our model in comparison to the IBM models, but, as shown in Section 3.1, it helps us to avoid what we call indirect associations, a major source of errors in other models.

Background

acl_arc_intent_classification:train:158

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Consequently, fusion has been applied to a wide variety of pattern recognition and decision theoretic problems— using a plethora of theories, techniques, and tools—including some applications in computational linguistics (e.g., Brill and Wu 1998; van Halteren, Zavrel, and Daelemans 1998) and speech technology (e.g., Bowles and Damper 1989; Romary and Pierre11989). Clearly, the above characterization is very wide ranging. Methods of information fusion include "voting methods, Bayesian inference, Dempster-Shafer 's method, generalized evidence processing theory, and various ad hoc techniques" (Hall 1992, 135). Citation Sentence: Consequently , fusion has been applied to a wide variety of pattern recognition and decision theoretic problems -- using a plethora of theories , techniques , and tools -- including some applications in computational linguistics ( e.g. , Brill and Wu 1998 ; van Halteren , Zavrel , and Daelemans 1998 ) and speech technology ( e.g. , Bowles and Damper 1989 ; Romary and Pierre11989 ) . Context after the citation: According to Abbott (1999, 290), "While the reasons [that] combining models works so well are not rigorously understood, there is ample evidence that improvements over single models are typical.... A strong case can be made for combining models across algorithm families as a means of providing uncorrelated output estimates." Our purpose in this paper is to study and exploit such fusion by model (or strategy) combination as a way of achieving performance gains in PbA. 6.

Background

acl_arc_intent_classification:train:1580

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This observation has led some researchers, e.g., Cooper and Paccia-Cooper (1980), to claim a direct mapping between the syntactic phrase and the prosodic phrase. (to pronounced /tu/) When it comes to sentence-level prosody, especially phrasing, it is often true, as we will see below, that a sequence of words dominated by the same syntactic node cohere more closely than a sequence of words dominated by two different nodes. Who did you speak to? Citation Sentence: This observation has led some researchers , e.g. , Cooper and Paccia-Cooper ( 1980 ) , to claim a direct mapping between the syntactic phrase and the prosodic phrase . Context after the citation: However, this claim is controversial because of the misa:ignments that occur between the two levels of phrasing. For example, in considering the connection between syntax and phrasing, the linguistic literature most often refers to examples of embedded sentences. Sentences like 12, from Chomsky (1965), are frequently cited. (Square brackets mark off the NP constituents that contain embedded sentences.)

Background

acl_arc_intent_classification:train:1581

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: These features are carefully designed to reduce the data sparseness problem and some of them are inspired by previous work (He et al., 2008; Gimpel and Smith, 2008; Marton and Resnik, 2008; Chiang et al., 2009; Setiawan et al., 2009; Shen et al., 2009; Xiong et al., 2009): 1. We incorporate into the ME based model the following informative context-based features to train CBSM and CBTM. ME approach has the merit of easily combining different features to predict the probability of each class. Citation Sentence: These features are carefully designed to reduce the data sparseness problem and some of them are inspired by previous work ( He et al. , 2008 ; Gimpel and Smith , 2008 ; Marton and Resnik , 2008 ; Chiang et al. , 2009 ; Setiawan et al. , 2009 ; Shen et al. , 2009 ; Xiong et al. , 2009 ) : 1 . Context after the citation: Function word features, which indicate whether the hierarchical source-side/targetside rule strings and sub-phrases covered by non-terminals contain function words that are often important clues of predicting syntactic structures. 2. POS features, which are POS tags of the boundary source words covered by nonterminals. 3.

Motivation

acl_arc_intent_classification:train:1582

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: In our previous work (Zhang and Chai, 2009), conversation entailment is formulated as the following: given a conversation segment D which is represented by a set of clauses D = d1 ∧ ... ∧ dm, and a hypothesis H represented by another set of clauses H = h1 ∧ ... ∧ hn, the prediction on whether D entails H is determined by the product of probabilities that each hypothesis clause hj is entailed from all the conversation segment clauses d1 ... dm as follows. Citation Sentence: In our previous work ( Zhang and Chai , 2009 ) , conversation entailment is formulated as the following : given a conversation segment D which is represented by a set of clauses D = d1 ∧ ... ∧ dm , and a hypothesis H represented by another set of clauses H = h1 ∧ ... ∧ hn , the prediction on whether D entails H is determined by the product of probabilities that each hypothesis clause hj is entailed from all the conversation segment clauses d1 ... dm as follows . Context after the citation: This is based on a simple assumption that whether a clause is entailed from a conversation segment is conditionally independent from other clauses. A clause here is similar to a sentence in firstorder predicate calculus. It is made up by terms and predicates. A term is either: 1) an entity described by a noun phrase, e.g., John Lennon, mother, or she; or 2) an action or event described by a verb phrase, e.g., marry in “John married Eva in 1940”.

Extends

acl_arc_intent_classification:train:1583

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Notable early papers on graph-based semisupervised learning include Blum and Chawla (2001), Bansal et al. (2002), Kondor and Lafferty (2002), and Joachims (2003). inter-document references in the form of hyperlinks (Agrawal et al., 2003). Previous sentiment-analysis work in different domains has considered inter-document similarity (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006) or explicit Citation Sentence: Notable early papers on graph-based semisupervised learning include Blum and Chawla ( 2001 ) , Bansal et al. ( 2002 ) , Kondor and Lafferty ( 2002 ) , and Joachims ( 2003 ) . Context after the citation: Zhu (2005) maintains a survey of this area. Recently, several alternative, often quite sophisticated approaches to collective classification have been proposed (Neville and Jensen, 2000; Lafferty et al., 2001; Getoor et al., 2002; Taskar et al., 2002; Taskar et al., 2003; Taskar et al., 2004; McCallum and Wellner, 2004). It would be interesting to investigate the application of such methods to our problem. However, we also believe that our approach has important advantages, including conceptual simplicity and the fact that it is based on an underlying optimization problem that is provably and in practice easy to solve.

Background

acl_arc_intent_classification:train:1584

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: In addition, we consider several types of lexical features (LexF) inspired by previous work on agreement and disagreement (Galley et al., 2004; Misra and Walker, 2013). The MI approach discovers the words that are highly associated with Agree/Disagree categories and these words turn to be useful features for classification. Compared to the all unigrams baseline, the MI-based unigrams improve the F1 by 4% (Agree) and 2% (Disagree) (Table 6). Citation Sentence: In addition , we consider several types of lexical features ( LexF ) inspired by previous work on agreement and disagreement ( Galley et al. , 2004 ; Misra and Walker , 2013 ) . Context after the citation: • Sentiment Lexicon (SL): Two features are designed using a sentiment lexicon (Hu and Liu, 2004) where the first feature represents the number of times the Callout and the Target contain a positive emotional word and the second feature represents the number of the negative emotional words. • Initial unigrams in Callout (IU): Instead of using all unigrams in the Callout and Target, we only select the first words from the Callout (maximum ten). The assumption is that the stance is generally expressed at the beginning of a Callout. We used the same MI-based technique to filter any sparse words.

Motivation

acl_arc_intent_classification:train:1585

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Berger et al. (2000) compared two retrieval approaches (TF.IDF and query expansion) and two predictive approaches (statistical translation and latent variable models). They complemented this approach with machine learning techniques that automatically learn the weights of different retrieval models. In FAQs, Berger and Mittal (2000) employed a sentence retrieval approach based on a language model where the entire response to an FAQ is considered a sentence, and the questions and answers are embedded in an FAQ document. Citation Sentence: Berger et al. ( 2000 ) compared two retrieval approaches ( TF.IDF and query expansion ) and two predictive approaches ( statistical translation and latent variable models ) . Context after the citation: Jijkoun and de Rijke (2005) compared different variants of retrieval techniques. Soricut and Brill (2006) compared a predictive approach (statistical translation), a retrieval approach based on a language-model, and a hybrid approach which combines statistical chunking and traditional retrieval. Two significant differences between help-desk and FAQs are the following. • The responses in the help-desk corpus are personalized, which means that on one hand, we must abstract from them sufficiently to obtain meaningful regularities, and on the other hand, we must be careful not to abstract away specific information that addresses particular issues.

Background

acl_arc_intent_classification:train:1586

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Others provide automatic mappings of natural language instructions to executable actions, such as interpreting navigation directions (Chen and Mooney, 2011) or robot commands (Tellex et al., 2011; Matuszek et al., 2012). Some approaches apply semantic parsing, where words and sentences are mapped to logical structure meaning (Kate and Mooney, 2007). The language grounding problem has come in many different flavors with just as many different approaches. Citation Sentence: Others provide automatic mappings of natural language instructions to executable actions , such as interpreting navigation directions ( Chen and Mooney , 2011 ) or robot commands ( Tellex et al. , 2011 ; Matuszek et al. , 2012 ) . Context after the citation: Some efforts have tackled tasks such as automatic image caption generation (Feng and Lapata, 2010a; Ordonez et al., 2011), text illustration (Joshi et al., 2006), or automatic location identification of Twitter users (Eisenstein et al., 2010; Wing and Baldridge, 2011; Roller et al., 2012). Another line of research approaches grounded language knowledge by augmenting distributional approaches of word meaning with perceptual information (Andrews et al., 2009; Steyvers, 2010; Feng and Lapata, 2010b; Bruni et al., 2011; Silberer and Lapata, 2012; Johns and Jones, 2012; Bruni et al., 2012a; Bruni et al., 2012b; Silberer et al., 2013). Although these approaches have differed in model definition, the general goal in this line of research has been to enhance word meaning with perceptual information in order to address one of the most common criticisms of distributional semantics: that the “meaning of words is entirely given by other words” (Bruni et al., 2012b). In this paper, we explore various ways to integrate new perceptual information through novel computational modeling of this grounded knowledge into a multimodal distributional model of word meaning.

Background

acl_arc_intent_classification:train:1587

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The dialogue state is represented by a cumulative answer analysis which tracks, over multiple turns, the correct, incorrect, and not-yet-mentioned parts 1Other factors such as student confidence could be considered as well (Callaway et al., 2007). Interaction between components is coordinated by the dialogue manager which uses the informationstate approach (Larsson and Traum, 2000). Citation Sentence: The dialogue state is represented by a cumulative answer analysis which tracks , over multiple turns , the correct , incorrect , and not-yet-mentioned parts 1Other factors such as student confidence could be considered as well ( Callaway et al. , 2007 ) . Context after the citation: of the answer. Once the complete answer has been accumulated, the system accepts it and moves on. Tutor hints can contribute parts of the answer to the cumulative state as well, allowing the system to jointly construct the solution with the student.

FutureWork

acl_arc_intent_classification:train:1588

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: As noted above, it is well documented (Roland and Jurafsky 1998) that subcategorization frames (and their frequencies) vary across domains. Another drawback to using an existing external gold standard such as COMLEX to evaluate an automatically induced subcategorization lexicon is that the resources are not necessarily constructed from the same source data. The first mapping is essentially a conflation of our more fine-grained LFG grammatical functions with the more generic COMLEX functions, while the second mapping tries to maintain as many distinctions as possible. Citation Sentence: As noted above , it is well documented ( Roland and Jurafsky 1998 ) that subcategorization frames ( and their frequencies ) vary across domains . Context after the citation: We have extracted frames from two sources (the WSJ and the Brown corpus), whereas COMLEX was built using examples from the San Jose Mercury News, the Brown corpus, several literary works from the Library of America, scientific abstracts from the U.S. Department of Energy, and the WSJ. For this reason, it is likely to contain a greater variety of subcategorization frames than our induced lexicon. It is also possible that because of human error, COMLEX contains subcategorization frames the validity of which are in doubt, for example, the overgeneration of subcategorized-for directional prepositional phrases. This is because the aim of the COMLEX project was to construct as complete a set of subcategorization frames as possible, even for infrequent verbs.

Motivation

acl_arc_intent_classification:train:1589

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: SWIZZLE is a multilingual enhancement of COCKTAIL (Harabagiu and Maiorano, 1999), a coreference resolution system that operates on a mixture of heuristics that combine semantic and textual cohesive information'. For both languages, we resolved coreference by using SWIZZLE, our implementation of a bilingual coreference resolver. Our claim is that by adding the wealth of coreferential features provided by multilingual data, new powerful heuristics for coreference resolution can be developed that outperform monolingual coreference resolution systems. Citation Sentence: SWIZZLE is a multilingual enhancement of COCKTAIL ( Harabagiu and Maiorano , 1999 ) , a coreference resolution system that operates on a mixture of heuristics that combine semantic and textual cohesive information ' . Context after the citation: When COCKTAIL was applied separately on the English and the Romanian texts, coreferring links were identified for each English and Romanian document respectively. When aligned referential expressions corefer with non-aligned anaphors, SWIZZLE derived new heuristics for coreference. Our experiments show that SWIZZLE outperformed COCKTAIL on both English and Romanian test documents. The rest of the paper is organized as follows.

Extends

acl_arc_intent_classification:train:159

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Chen and Vijay-Shanker (2000) explore a number of related approaches to the extraction of a lexicalized TAG from the Penn-II Treebank with the aim of constructing a statistical model for parsing. As these formalisms are fully lexicalized with an invariant (LTAG and CCG) or limited (HPSG) rule component, the extraction of a lexicon essentially amounts to the creation of a grammar. Work has been carried out on the extraction of formalism-specific lexical resources from the Penn-II Treebank, in particular TAG, CCG, and HPSG. Citation Sentence: Chen and Vijay-Shanker ( 2000 ) explore a number of related approaches to the extraction of a lexicalized TAG from the Penn-II Treebank with the aim of constructing a statistical model for parsing . Context after the citation: The extraction procedure utilizes a head percolation table as introduced by Magerman (1995) in combination with a variation of Collins’s (1997) approach to the differentiation between complement and adjunct. This results in the construction of a set of lexically anchored elementary trees which make up the TAG in question. The number of frame types extracted (i.e., an elementary tree without a specific lexical anchor) ranged from 2,366 to 8,996. Xia (1999) also presents a similar method for the extraction of a TAG from the Penn Treebank.

Background

acl_arc_intent_classification:train:1590

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: At the same time, we believe our method has advantages over the approach developed initially at IBM (Brown et al. 1990; Brown et al. 1993) for training translation systems automatically. Compared with left-to-right transduction, middle-out transduction also aids robustness because, when complete derivations are not available, partial derivations tend to have meaningful headwords. The reduction of effort results, in large part, from being able to do without artificial intermediate representations of meaning; we do not require the development of semantic mapping rules (or indeed any rules) or the creation of a corpus including semantic annotations. Citation Sentence: At the same time , we believe our method has advantages over the approach developed initially at IBM ( Brown et al. 1990 ; Brown et al. 1993 ) for training translation systems automatically . Context after the citation: One advantage is that our method attempts to model the natural decomposition of sentences into phrases. Another is that the compilation of this decomposition into lexically anchored finite-state head transducers produces implementations that are much more efficient than those for the IBM model. In particular, our search algorithm finds optimal transductions of test sentences in less than "real time" on a 300MHz processor, that is, the time to translate an utterance is less than the time taken to speak it, an important consideration for our speech translation application.

CompareOrContrast

acl_arc_intent_classification:train:1591

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Building on the work of Ruch et al. (2003) in the same domain, we present a generative approach that attempts to directly model the discourse structure of MEDLINE abstracts using Hidden Markov Models (HMMs); cfXXX (Barzilay and Lee, 2004). McKnight and Srinivasan (2003) have previously examined the task of categorizing sentences in medical abstracts using supervised discriminative machine learning techniques. Furthermore, the availability of rich ontological resources, in the form of the Unified Medical Language System (UMLS) (Lindberg et al., 1993), and the availability of software that leverages this knowledge— MetaMap (Aronson, 2001) for concept identification and SemRep (Rindflesch and Fiszman, 2003) for relation extraction—provide a foundation for studying the role of semantics in various tasks. Citation Sentence: Building on the work of Ruch et al. ( 2003 ) in the same domain , we present a generative approach that attempts to directly model the discourse structure of MEDLINE abstracts using Hidden Markov Models ( HMMs ) ; cfXXX ( Barzilay and Lee , 2004 ) . Context after the citation: Although our results were not obtained from the same exact collection as those used by authors of these two previous studies, comparable experiments suggest that our techniques are competitive in terms of performance, and may offer additional advantages as well. Discriminative approaches (especially SVMs) have been shown to be very effective for many supervised classification tasks; see, for example, (Joachims, 1998; Ng and Jordan, 2001). However, their high computational complexity (quadratic in the number of training samples) renders them prohibitive for massive data processing. Under certain conditions, generative approaches with linear complexity are preferable, even if their performance is lower than that which can be achieved through discriminative training.

Background

acl_arc_intent_classification:train:1592

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The basic Python reflection has already been implemented and used for large scale experiments with POS tagging, using pyMPI (a message passing interface library for Python) to coordinate experiments across a cluster of over 100 machines (Curran and Clark, 2003; Clark et al., 2003). Finally, since Python can produce stand-alone executables directly, it will be possible to create distributable code that does not require the entire infrastructure or Python interpreter to be installed. The Python interface allows the components to be dynamically composed, configured and extended in any operating system environment without the need for a compiler. Citation Sentence: The basic Python reflection has already been implemented and used for large scale experiments with POS tagging , using pyMPI ( a message passing interface library for Python ) to coordinate experiments across a cluster of over 100 machines ( Curran and Clark , 2003 ; Clark et al. , 2003 ) . Context after the citation: An example of using the Python tagger interface is shown in Figure 1. On top of the Python interface we plan to implement a GUI interface for composing and configuring components. This will be implemented in wxPython which is a platform independent GUI library that uses the native windowing environment under Windows, MacOS and most versions of Unix. The wxPython interface will generate C++ and Python code that composes and configures the components.

Background

acl_arc_intent_classification:train:1593

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: As an alternative, we rely on PubMed to retrieve an initial set of hits that we then postprocess in greater detail—this is the standard pipeline architecture commonly employed in other question-answering systems (Voorhees and Tice 1999; Hirschman and Gaizauskas 2001). However, we do not have access to the computational resources necessary to apply knowledge extractors to the 15 million plus citations in the MEDLINE database and directly index their results. Ideally, we would like to match structured representations derived from the question with those derived from MEDLINE citations (taking into consideration other EBMrelevant factors). Citation Sentence: As an alternative , we rely on PubMed to retrieve an initial set of hits that we then postprocess in greater detail -- this is the standard pipeline architecture commonly employed in other question-answering systems ( Voorhees and Tice 1999 ; Hirschman and Gaizauskas 2001 ) . Context after the citation: The architecture of our system is shown in Figure 1. The query formulator is responsible for converting a clinical question (in the form of a query frame) into a PubMed search query. Presently, these queries are already encoded in our test collection (see Section 6). PubMed returns an initial list of MEDLINE citations, which is then analyzed by our knowledge extractors (see Section 5).

CompareOrContrast

acl_arc_intent_classification:train:1594

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: The language grounding problem has received significant attention in recent years, owed in part to the wide availability of data sets (e.g. Flickr, Von Ahn (2006)), computing power, improved computer vision models (Oliva and Torralba, 2001; Lowe, 2004; Farhadi et al., 2009; Parikh and Grauman, 2011) and neurological evidence of ties between the language, perceptual and motor systems in the brain (Pulverm¨uller et al., 2005; Tettamanti et al., 2005; Aziz-Zadeh et al., 2006). Citation Sentence: The language grounding problem has received significant attention in recent years , owed in part to the wide availability of data sets ( e.g. Flickr , Von Ahn ( 2006 ) ) , computing power , improved computer vision models ( Oliva and Torralba , 2001 ; Lowe , 2004 ; Farhadi et al. , 2009 ; Parikh and Grauman , 2011 ) and neurological evidence of ties between the language , perceptual and motor systems in the brain ( Pulverm ¨ uller et al. , 2005 ; Tettamanti et al. , 2005 ; Aziz-Zadeh et al. , 2006 ) . Context after the citation: Many approaches to multimodal research have succeeded by abstracting away raw perceptual information and using high-level representations instead. Some works abstract perception via the usage of symbolic logic representations (Chen et al., 2010; Chen and Mooney, 2011; Matuszek et al., 2012; Artzi and Zettlemoyer, 2013), while others choose to employ concepts elicited from psycholinguistic and cognition studies. Within the latter category, the two most common representations have been association norms, where subjects are given a 1http://stephenroller.com/research/ emnlp13

Background

acl_arc_intent_classification:train:1595

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Although in this paper we take modus ponens as the main rule of inference, in general one can consider deductive closures with respect to weaker, nonstandard logics, (cfXXX Levesque 1984; Frisch 1987; Patel-Schneider 1985). 0 is a ground instance of a formula 0, if 0 contains no variables, and 0 = 00, for some substitution O. Thus, we do not require Th(T) to be closed under substitution instances of tautologies. Form(F). Citation Sentence: Although in this paper we take modus ponens as the main rule of inference , in general one can consider deductive closures with respect to weaker , nonstandard logics , ( cfXXX Levesque 1984 ; Frisch 1987 ; Patel-Schneider 1985 ) . Context after the citation: But we won't pursue this topic further here.

CompareOrContrast

acl_arc_intent_classification:train:1596

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: As Alshawi (1987) points out, given that no situations were envisaged where the information from the tape would be altered once installed in secondary storage, this simple and convenComputational Linguistics, Volume 13, Numbers 3-4, July-December 1987 205 Bran Boguraev and Ted Briscoe Large Lexicons for Natural Language Processing tional access strategy is perfectly adequate. A dictionary access process is fired off, which dynamically constructs a search tree and navigates through it from a given homograph directly to the offset in the lispified file from where all the associated information can be retrieved. They all make use of an efficient dictionary access system which services requests for s-expression entries made by client programs. Citation Sentence: As Alshawi ( 1987 ) points out , given that no situations were envisaged where the information from the tape would be altered once installed in secondary storage , this simple and convenComputational Linguistics , Volume 13 , Numbers 3-4 , July-December 1987 205 Bran Boguraev and Ted Briscoe Large Lexicons for Natural Language Processing tional access strategy is perfectly adequate . Context after the citation: The use of such standard database indexing techniques makes it possible for an active dictionary process to be very undemanding with respect to main memory utilisation. For reasons of efficiency and flexibility of customisation, namely the use of LDOCE by different client programs and from different Lisp and/or Prolog systems, the dictionary access system is implemented in the programming language C and makes use of the inter-process communication facilities provided by the Unix operating system. To the Lisp programmer, the creation of a dictionary process and subsequent requests for information from the dictionary appear simply as Lisp function calls. Most of the recent work with the dictionary, and in particular the decompacting and analysis of the grammar codes has been carried out in Interlisp-D on Xerox 1100 series workstations.

Background

acl_arc_intent_classification:train:1597

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Other factors, such as the role of focus (Grosz 1977, 1978; Sidner 1983) or quantifier scoping (Webber 1983) must play a role, too. They are intended as an illustration of the power of abduction, which in this framework helps determine the universe of the model (that is the set of entities that appear in it). We have no doubts that various other metarules will be necessary; clearly, our two metarules cannot constitute the whole theory of anaphora resolution. Citation Sentence: Other factors , such as the role of focus ( Grosz 1977 , 1978 ; Sidner 1983 ) or quantifier scoping ( Webber 1983 ) must play a role , too . Context after the citation: Determining the relative importance of those factors, the above metarules, and syntactic clues, appears to be an interesting topic in itself. Note: In our translation from English to logic we are assuming that "it" is anaphoric (with the pronoun following the element that it refers to), not cataphoric (the other way around). This means that the "it" that brought the disease in P1 will not be considered to refer to the infection "i" or the death "d" in P3. This strategy is certainly the right one to start out with, since anaphora is always the more typical direction of reference in English prose (Halliday and Hasan 1976, p. 329).

Background

acl_arc_intent_classification:train:1598

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This paper describes an approach for sharing resources in various grammar formalisms such as Feature-Based Lexicalized Tree Adjoining Grammar (FB-LTAG1) (Vijay-Shanker, 1987; Vijay-Shanker and Joshi, 1988) and Head-Driven Phrase Structure Grammar (HPSG) (Pollard and Sag, 1994) by a method of grammar conversion. Citation Sentence: This paper describes an approach for sharing resources in various grammar formalisms such as Feature-Based Lexicalized Tree Adjoining Grammar ( FB-LTAG1 ) ( Vijay-Shanker , 1987 ; Vijay-Shanker and Joshi , 1988 ) and Head-Driven Phrase Structure Grammar ( HPSG ) ( Pollard and Sag , 1994 ) by a method of grammar conversion . Context after the citation: The RenTAL system automatically converts an FB-LTAG grammar into a strongly equivalent HPSG-style grammar (Yoshinaga and Miyao, 2001). Strong equivalence means that both grammars generate exactly equivalent parse results, and that we can share the LTAG grammars and lexicons in HPSG applications. Our system can reduce considerable workload to develop a huge resource (grammars and lexicons) from scratch. Our concern is, however, not limited to the sharing of grammars and lexicons.

Background

acl_arc_intent_classification:train:1599

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: McKnight and Srinivasan (2003) have previously examined the task of categorizing sentences in medical abstracts using supervised discriminative machine learning techniques. Furthermore, the availability of rich ontological resources, in the form of the Unified Medical Language System (UMLS) (Lindberg et al., 1993), and the availability of software that leverages this knowledge— MetaMap (Aronson, 2001) for concept identification and SemRep (Rindflesch and Fiszman, 2003) for relation extraction—provide a foundation for studying the role of semantics in various tasks. (NLM), which also serves as a readily available corpus of abstracts for our experiments. Citation Sentence: McKnight and Srinivasan ( 2003 ) have previously examined the task of categorizing sentences in medical abstracts using supervised discriminative machine learning techniques . Context after the citation: Building on the work of Ruch et al. (2003) in the same domain, we present a generative approach that attempts to directly model the discourse structure of MEDLINE abstracts using Hidden Markov Models (HMMs); cfXXX (Barzilay and Lee, 2004). Although our results were not obtained from the same exact collection as those used by authors of these two previous studies, comparable experiments suggest that our techniques are competitive in terms of performance, and may offer additional advantages as well. Discriminative approaches (especially SVMs) have been shown to be very effective for many supervised classification tasks; see, for example, (Joachims, 1998; Ng and Jordan, 2001). However, their high computational complexity (quadratic in the number of training samples) renders them prohibitive for massive data processing.

Background

acl_arc_intent_classification:train:16

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: (Davis and Ogden, 1997; Ballesteros and Croft, 1997; Hull and (3refenstette, 1996). Another common approach is term translation, e.g., via a bilingual lexicon. Our focus is on languages where no MT exists, but a bilingual dictionary may exist or may be derived. Citation Sentence: ( Davis and Ogden , 1997 ; Ballesteros and Croft , 1997 ; Hull and ( 3refenstette , 1996 ) . Context after the citation: While word sense disambiguation has been a central topic in previous studies for cross-lingual IR, our study suggests that using multiple weighted translations and compensating for the incompleteness of the lexicon may be more valuable. Other studies on the value of disambiguation for cross-lingual IR include Hiemstra and de Jong, 1999; Hull, 1997. Sanderson, 1994 studied the issue of disambiguation for mono-lingual M. The third approach to cross-lingual retrieval is to map queries and documents to some intermediate representation, e.g latent semantic indexing (LSI) (Littman et al, 1998), or the General Vector space model (GVSM), (Carbonell et al, 1997).

CompareOrContrast

acl_arc_intent_classification:train:160

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: This is because the binary structure has been verified to be very effective for tree-based translation (Wang et al., 2007; Zhang et al., 2011a). Differently, we require that each multi-word non-terminal node must have two child nodes. To generate frag, Cohn and Blunsom (2009) used a geometric prior to decide how many child nodes to assign each node. Citation Sentence: This is because the binary structure has been verified to be very effective for tree-based translation ( Wang et al. , 2007 ; Zhang et al. , 2011a ) . Context after the citation: The generation process starts at root node N. At first, root node N is expanded into two child nodes. Then, each newly generated node will be checked to expand into two new child nodes with probability pexpand. This process repeats until all the new non-terminal nodes are checked.

Motivation

acl_arc_intent_classification:train:1600

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: There is a general consensus among theoretical linguists that the proper representation of verbal argument structure is event structure—representations grounded in a theory of events that decompose semantic roles in terms of primitive predicates representing concepts such as causality and inchoativity (Dowty, 1979; Jackendoff, 1983; Pustejovsky, 1991b; Rappaport Hovav and Levin, 1998). Fixed roles are too coarsegrained to account for certain semantic distinctions—the only recourse, to expand the inventory of roles, comes with the price of increased complexity, e.g., in the syntaxto-semantics mapping. The actual inventory of semantic roles, along with precise definitions and diagnostics, remains an unsolved problem; see (Levin and Rappaport Hovav, 1996). Citation Sentence: There is a general consensus among theoretical linguists that the proper representation of verbal argument structure is event structure -- representations grounded in a theory of events that decompose semantic roles in terms of primitive predicates representing concepts such as causality and inchoativity ( Dowty , 1979 ; Jackendoff , 1983 ; Pustejovsky , 1991b ; Rappaport Hovav and Levin , 1998 ) . Context after the citation: Consider the following example: (2) He sweeps the floor clean. [ [ DO(he, sweeps(the floor)) ] CAUSE [ BECOME [ clean(the floor) ] ] ] Dowty breaks the event described by (2) into two subevents, the activity of sweeping the floor and its result, the state of the floor being clean. A more recent approach, advocated by Rappaport Hovav and Levin (1998), describes a basic set of event templates corresponding to Vendler’s event classes (Vendler, 1957): (3) a. [ x ACT<MANNER> ] (activity) b. [ x <STATE> ] (state) c. [ BECOME [ x <STATE> ] ] (achievement) d. [ x CAUSE [ BECOME [ x <STATE> ] ] ] (accomplishment)

Background

acl_arc_intent_classification:train:1601

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: measure the standard intrinsic parser metrics unlabeled attachment score (UAS) and labeled attachment score (LAS) (Buchholz and Marsi, 2006). For some experiments we also We show empirical results for two extrinsic loss-functions (optimizing for the downstream task): machine translation and domain adaptation; and for one intrinsic loss-function: an arclength parsing score. Citation Sentence: measure the standard intrinsic parser metrics unlabeled attachment score ( UAS ) and labeled attachment score ( LAS ) ( Buchholz and Marsi , 2006 ) . Context after the citation: In terms of treebank data, the primary training corpus is the Penn Wall Street Journal Treebank (PTB) (Marcus et al., 1993). We also make use of the Brown corpus, and the Question Treebank (QTB) (Judge et al., 2006). For PTB and Brown we use standard training/development/testing splits of the data. For the QTB we split the data into three sections: 2000 training, 1000 development, and 1000 test.

Uses

acl_arc_intent_classification:train:1602

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The EDR has close ties to the named entity recognition (NER) and coreference resolution tasks, which have been the focus of several recent investigations (Bikel et al., 1997; Miller et al., 1998; Borthwick, 1999; Mikheev et al., 1999; Soon et al., 2001; Ng and Cardie, 2002; Florian et al., 2004), and have been at the center of evaluations such as: MUC-6, MUC-7, and the CoNLL'02 and CoNLL'03 shared tasks. In this paper we focus on the Entity Detection and Recognition task (EDR) for Arabic as described in ACE 2004 framework (ACE, 2004). These tasks have applications in summarization, information retrieval (one can get all hits for Washington/person and not the ones for Washington/state or Washington/city), data mining, question answering, language understanding, etc. Citation Sentence: The EDR has close ties to the named entity recognition ( NER ) and coreference resolution tasks , which have been the focus of several recent investigations ( Bikel et al. , 1997 ; Miller et al. , 1998 ; Borthwick , 1999 ; Mikheev et al. , 1999 ; Soon et al. , 2001 ; Ng and Cardie , 2002 ; Florian et al. , 2004 ) , and have been at the center of evaluations such as : MUC-6 , MUC-7 , and the CoNLL '02 and CoNLL '03 shared tasks . Context after the citation: Usually, in computational linguistics literature, a named entity is an instance of a location, a person, or an organization, and the NER task consists of identifying each of these occurrences. Instead, we will adopt the nomenclature of the Automatic Content Extraction program (NIST, 2004): we will call the instances of textual references to objects/abstractions mentions, which can be either named (e.g. John Mayor), nominal (the president) or pronominal (she, it). An entity is the aggregate of all the mentions (of any level) which refer to one conceptual entity. For instance, in the sentence

Background

acl_arc_intent_classification:train:1603

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Accordingly, we convert examples such as (27) into their generalized equivalents, as in (28): (28) <DET> good man: bon homme That is, where Block (2000) substitutes variables for various words in his templates, we replace certain lexical items with their marker tag. In our system, in some cases the smallest chunk obtainable via the marker-based segmentation process may be something like (27): (27) <DET> the good man: le bon homme In such cases, if our system were confronted with a good man, it would not be able to translate such a phrase, assuming this to be missing from the marker lexicon. Other similar approaches include those of Cicekli and G¨uvenir (1996), McTait and Trujillo (1999), Carl (1999), and Brown (2000), inter alia. Citation Sentence: Accordingly , we convert examples such as ( 27 ) into their generalized equivalents , as in ( 28 ) : ( 28 ) <DET> good man : bon homme That is , where Block ( 2000 ) substitutes variables for various words in his templates , we replace certain lexical items with their marker tag . Context after the citation: Given that examples such as ’‘<DET> a : un” are likely to exist in the word-level lexicon, they may be inserted at the point indicated by the marker tag to form the correct translation un bon homme. We thus cluster on marker words to improve the coverage of our system (see Section 5 for results that show exactly how clustering on marker words helps); others (notably Brown [2000, 2003]) use clustering techniques to determine equivalence classes of individual words that can occur in the same context, and in so doing derive translation templates from individual translation examples.

CompareOrContrast

acl_arc_intent_classification:train:1604

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: How it is done is beyond the scope of this paper but is explained in detail in Fink (1983). The comparison that must be made to determine which option is better in a given situation is how often the first will error correct incorrectly as opposed to how much error correcting power we will lose by using the second. Thus, both options are imperfect in terms of the error correction capabilities that they can provide. Citation Sentence: How it is done is beyond the scope of this paper but is explained in detail in Fink ( 1983 ) . Context after the citation: The Merge function takes two inputs, M1 and M2, which have been determined by the Mergeable function to be similar in some way by considering their respective environments and meanings. Based upon how similar the two meanings are, Merge creates a meaning M that is a generalization of M1 and M2, sometimes employing an argument. Thus, there are only two possible kinds of matches at this point between an input sentence and a member of the expected sentence set, an exact match or a similar match. In the case of an exact match M = M1 = M2 and M replaces M1 in the expected dialogue.

Background

acl_arc_intent_classification:train:1605

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: These tools use a highly optimised GIS implementation and provide sophisticated Gaussian smoothing (Chen and Rosenfeld, 1999). These tools currently train in less than 10 minutes on the standard training materials and tag faster than TNT, the fastest existing POS tagger. We have already implemented a POS tagger, chunker, CCG supertagger and named entity recogniser using the infrastructure. Citation Sentence: These tools use a highly optimised GIS implementation and provide sophisticated Gaussian smoothing ( Chen and Rosenfeld , 1999 ) . Context after the citation: We expect even faster training times when we move to conjugate gradient methods. The next step of the process will be to add different statistical models and machine learning methods. We first plan to add a simple Naive Bayes model to the system. This will allow us to factor out the maximum entropy specific parts of the system and produce a general component for statistical modelling.

Uses

acl_arc_intent_classification:train:1606

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Also relevant is work on the general problems of dialog-act tagging (Stolcke et al., 2000), citation analysis (Lehnert et al., 1990), and computational rhetorical analysis (Marcu, 2000; Teufel and Moens, 2002). More sophisticated approaches have been proposed (Hillard et al., 2003), including an extension that, in an interesting reversal of our problem, makes use of sentimentpolarity indicators within speech segments (Galley et al., 2004). Detecting agreement We used a simple method to learn to identify cross-speaker references indicating agreement. Citation Sentence: Also relevant is work on the general problems of dialog-act tagging ( Stolcke et al. , 2000 ) , citation analysis ( Lehnert et al. , 1990 ) , and computational rhetorical analysis ( Marcu , 2000 ; Teufel and Moens , 2002 ) . Context after the citation: We currently do not have an efficient means to encode disagreement information as hard constraints; we plan to investigate incorporating such information in future work. Relationships between the unlabeled items Carvalho and Cohen (2005) consider sequential relations between different types of emails (e.g., between requests and satisfactions thereof) to classify messages, and thus also explicitly exploit the structure of conversations. Previous sentiment-analysis work in different domains has considered inter-document similarity (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006) or explicit inter-document references in the form of hyperlinks (Agrawal et al., 2003).

Background

acl_arc_intent_classification:train:1607

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The inference rules that were necessary to convert one list of properties into another do not sit comfortably within the received NLG pipeline model (e.g., Reiter and Dale 2000). Architecture (Section 6). Much is still unknown, differences between speakers abound, and the experimental methodology for advancing the state of the art in this area is not without its problems (van Deemter 2004). Citation Sentence: The inference rules that were necessary to convert one list of properties into another do not sit comfortably within the received NLG pipeline model ( e.g. , Reiter and Dale 2000 ) . Context after the citation: An example of such an inference rule is the one that transforms a list of the form (mouse, >10 cm) into one of the form (mouse, size(x) = max2) if only two mice are larger than 10 cm. The same issues also make it difficult to interleave CD and linguistic realization as proposed by various authors, because properties may need to be combined before they are expressed. Incrementality (Section 8). Gradable adjectives complicate the notion of incrementality, in generation as well as interpretation.

Background

acl_arc_intent_classification:train:1608

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Niyogi (2001) has developed an agenda-driven chart parser for the feature-driven formalism described above; please refer to his paper for a description of the parsing algorithm. A simple example of this is the case assignment involved in building a prepositional phrase, i.e., prepositions must assign case, and DPs much receive case. The +x denotes a need to discharge features, and the -x denotes a need for features. Citation Sentence: Niyogi ( 2001 ) has developed an agenda-driven chart parser for the feature-driven formalism described above ; please refer to his paper for a description of the parsing algorithm . Context after the citation: I have adapted it for my needs and developed grammar fragments that reflect my non-lexicalist semantic framework. As an example, a simplified derivation of the sentence “The tire flattened.” is shown in Figure 1. The currently implemented system is still at the “toy parser” stage.

Extends

acl_arc_intent_classification:train:1609

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: de URL: http://www.sfs.nphil.uni-tuebingen.de/sfb /b4home.html 1 This is, for example, the case for all proposals working with verbal lexical entries that raise the arguments of a verbal complement (Hinrichs and Nakazawa 1989) that also use lexical rules such as the Complement Extraction Lexical Rule (Pollard and Sag 1994) or the Complement Cliticization Lexical Rule (Miller and Sag 1993) to operate on those raised elements. nphil.uni-tuebingen. email: {dm,minnen}@sfs. Citation Sentence: de URL : http://www.sfs.nphil.uni-tuebingen.de/sfb / b4home.html 1 This is , for example , the case for all proposals working with verbal lexical entries that raise the arguments of a verbal complement ( Hinrichs and Nakazawa 1989 ) that also use lexical rules such as the Complement Extraction Lexical Rule ( Pollard and Sag 1994 ) or the Complement Cliticization Lexical Rule ( Miller and Sag 1993 ) to operate on those raised elements . Context after the citation: Also an analysis treating adjunct extraction via lexical rules (van Noord and Bouma 1994) results in an infinite lexicon. Treatments of lexical rules as unary phrase structure rules also require their fully explicit specification, which entails the last problem mentioned above. In addition, computationally treating lexical rules on a par with phrase structure rules fails to take computational advantage of their specific properties. For example, the interaction of lexical rules is explored at run-time, even though the possible interaction can be determined at compile-time given the information available in the lexical rules and the base lexical entries.2 Based on the research results reported in Meurers and Minnen (1995, 1996), we propose a new computational treatment of lexical rules that overcomes these shortcomings and results in a more efficient processing of lexical rules as used in HPSG.

Background

acl_arc_intent_classification:train:161

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: An example of psycholinguistically oriented research work can be found in Bond and Hayes (1983). Although these dictates are fairly clear, the underlying notion of topic is not. In these sources, a paragraph is notionally defined as something like a series of sentences that develop one single topic, and rules are laid down for the construction of an ideal (or at least an acceptable) paragraph. Citation Sentence: An example of psycholinguistically oriented research work can be found in Bond and Hayes ( 1983 ) . Context after the citation: These authors take the position that a paragraph is a psychologically real unit of discourse, and, in fact, a formal grammatical unit. Bond and Hayes found three major formal devices that are used, by readers, to identify a paragraph: (1) the repetition of content words (nouns, verbs, adjectives, adverbs); (2) pronoun reference; and (3) paragraph length, as determined by spatial and/or sentence-count information. Other psycholing-uistic studies that confirm the validity of paragraph units can be found in Black and Bower (1979) and Haberlandt et al. (1980). The textualist approach to paragraph analysis is exemplified by E. J. Crothers.

Background

acl_arc_intent_classification:train:1610

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In addition to a referring function, noun phrases (NP) can also serve communicative goals such as providing new information about the referent and expressing the speaker's emotional attitude towards the referent (Appelt, 1985; Jordan, 2000). Citation Sentence: In addition to a referring function , noun phrases ( NP ) can also serve communicative goals such as providing new information about the referent and expressing the speaker 's emotional attitude towards the referent ( Appelt , 1985 ; Jordan , 2000 ) . Context after the citation: In Example (1) below, the part in italics refers to an object in a museum, and the part in boldface provides additional information about it. (1) This example from the time of the Qianlong Emperor 1736-95 is made of lacquered wood with decoration in gold and red. Such complex NPs appear frequently in human written texts. A natural language generation (NLG) system must be able to produce complex NPs serving multiple goals in order to write texts as humans do.

Background

acl_arc_intent_classification:train:1611

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Some well-known approaches include rule-based models (Brill and Resnik 1994), backed-off models (Collins and Brooks 1995), and a maximumentropy model (Ratnaparkhi 1998). Researchers have proposed many computational models for resolving PPattachment ambiguities. One common source of structural ambiguities arises from syntactic constructs in which a prepositional phrase might be equally likely to modify the verb or the noun preceding it. Citation Sentence: Some well-known approaches include rule-based models ( Brill and Resnik 1994 ) , backed-off models ( Collins and Brooks 1995 ) , and a maximumentropy model ( Ratnaparkhi 1998 ) . Context after the citation: Following the tradition of using learning PPattachment as a way to gain insight into the parsing problem, we first apply sample selection to reduce the amount of annotation used in training a PP-attachment model. We use the Collins-Brooks model as the basic learning algorithm and experiment with several evaluation functions based on the types of predictive criteria described earlier. Our experiments show that the best evaluation function can reduce the number of labeled examples by nearly half without loss of accuracy.

Background

acl_arc_intent_classification:train:1612

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: There is some literature on procedure acquisition such as the LISP synthesis work described in Biermann et al. (1984) and the PROLOG synthesis method of Shapiro (1982). That is, the current system learns procedures rather than data structures. The current system learns finite state flowcharts whereas typical learning systems usually acquire coefficient values as in Minsky and Papert (1969), assertional statements as in Michalski (1980), or semantic nets as in Winston (1975). Citation Sentence: There is some literature on procedure acquisition such as the LISP synthesis work described in Biermann et al. ( 1984 ) and the PROLOG synthesis method of Shapiro ( 1982 ) . Context after the citation: However, the latter methodologies have not been applied to dialogue acquisition.

CompareOrContrast

acl_arc_intent_classification:train:1613

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Against the background of a growing interest in multilingual NLP, multilingual anaphora /coreference resolution has gained considerable momentum in recent years (Aone and McKee 1993; Azzam, Humphreys, and Gaizauskas 1998; Harabagiu and Maiorano 2000; Mitkov and Barbu 2000; Mitkov 1999; Mitkov and Stys 1997; Mitkov, Belguith, and Stys 1998). The last decade of the 20th century saw a number of anaphora resolution projects for languages other than English such as French, German, Japanese, Spanish, Portuguese, and Turkish. The inclusion of the coreference task in the Sixth and Seventh Message Understanding Conferences (MUC-6 and MUC-7) gave a considerable impetus to the development of coreference resolution algorithms and systems, such as those described in Baldwin et al. (1995), Gaizauskas and Humphreys (1996), and Kameyama (1997). Citation Sentence: Against the background of a growing interest in multilingual NLP , multilingual anaphora / coreference resolution has gained considerable momentum in recent years ( Aone and McKee 1993 ; Azzam , Humphreys , and Gaizauskas 1998 ; Harabagiu and Maiorano 2000 ; Mitkov and Barbu 2000 ; Mitkov 1999 ; Mitkov and Stys 1997 ; Mitkov , Belguith , and Stys 1998 ) . Context after the citation: Other milestones of recent research include the deployment of probabilistic and machine learning techniques (Aone and Bennett 1995; Kehler 1997; Ge, Hale, and Charniak 1998; Cardie and Wagstaff 1999; the continuing interest in centering, used either in original or in revised form (Abracos and Lopes 1994; Strube and Hahn 1996; Hahn and Strube 1997; Tetreault 1999); and proposals related to the evaluation methodology in anaphora resolution (Mitkov 1998a, 2001b). For a more detailed survey of the state of the art in anaphora resolution, see Mitkov (forthcoming). The papers published in this issue reflect the major trends in anaphora resolution in recent years. Some of them describe approaches that do not exploit full syntactic knowledge (as in the case of Palomar et al.'s and Stuckardt's work) or that employ machine learning techniques (Soon, Ng, and Lim); others present centering-based pronoun resolution (Tetreault) or discuss theoretical centering issues (Kibble).

Background

acl_arc_intent_classification:train:1614

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The list of semantic relations with which we work is based on extensive literature study (Barker et al., 1997a). Citation Sentence: The list of semantic relations with which we work is based on extensive literature study ( Barker et al. , 1997a ) . Context after the citation: Three lists of relations for three syntactic levels – inter-clause, intra-clause (case) and nounmodifier relations – were next combined based on syntactic and semantic phenomena. The resulting list is the one used in the experiments we present in this paper. The relations are grouped by general similarity into 6 relation classes (H denotes the head of a base NP, M denotes the modifier). 1.

Uses

acl_arc_intent_classification:train:1615

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The research described below is taking place in the context of three collaborative projects (Boguraev, 1987; Russell et al., 1986; Phillips and Thompson, 1986) to develop a general-purpose, wide coverage morphological and syntactic analyser for English. Real-time parsing imposes stringent requirements on a dictionary support environment; at the very least it must allow frequent and rapid access to the information in the dictionary via the dictionary head words. These developments also emphasise that if natural language processing systems are to be able to handle the grammatical and semantic idiosyncracies of individual lexical items elegantly and efficiently, then the lexicon must be a central component of the parsing system. Citation Sentence: The research described below is taking place in the context of three collaborative projects ( Boguraev , 1987 ; Russell et al. , 1986 ; Phillips and Thompson , 1986 ) to develop a general-purpose , wide coverage morphological and syntactic analyser for English . Context after the citation: One motivation for our interest in machine readable dictionaries is to attempt to provide a substantial lexicon with lexical entries containing grammatical information compatible with the grammatical framework employed by the analyser. The idea of using the machine readable source of a published dictionary has occurred to a wide range of researchers, for spelling correction, lexical analysis, thesaurus construction, and machine translation, to name but a few applications. Most of the work on automated dictionaries has concentrated on extracting lexical or other information, essentially by batch processing (eg. Amsler, 1981; Walker and Amsler, 1986), or

Background

acl_arc_intent_classification:train:1616

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In most cases, the accuracy of parsers degrades when run on out-of-domain data (Gildea, 2001; McClosky et al., 2006; Blitzer et al., 2006; Petrov et al., 2010). This includes work on question answering (Wang et al., 2007), sentiment analysis (Nakagawa et al., 2010), MT reordering (Xu et al., 2009), and many other tasks. The accuracy and speed of state-of-the-art dependency parsers has motivated a resumed interest in utilizing the output of parsing as an input to many downstream natural language processing tasks. Citation Sentence: In most cases , the accuracy of parsers degrades when run on out-of-domain data ( Gildea , 2001 ; McClosky et al. , 2006 ; Blitzer et al. , 2006 ; Petrov et al. , 2010 ) . Context after the citation: But these accuracies are measured with respect to gold-standard out-of-domain parse trees. There are few tasks that actually depend on the complete parse tree. Furthermore, when evaluated on a downstream task, often the optimal parse output has a model score lower than the best parse as predicted by the parsing model. While this means that we are not properly modeling the downstream task in the parsers, it also means that there is some information from small task or domain-specific data sets which could help direct our search for optimal parameters during parser training.

Background

acl_arc_intent_classification:train:1617

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This paper presents experiments with generative content models for analyzing the discourse structure of medical abstracts, which has been confirmed to follow the four-section pattern discussed above (Salanger-Meyer, 1990). Demner-Fushman et al. (2005) found a correlation between the quality and strength of clinical conclusions in the full article texts and abstracts. For example, Gay et al. (2005) experimented with abstracts and full article texts in the task of automatically generating index term recommendations and discovered that using full article texts yields at most a 7.4% improvement in F-score. Citation Sentence: This paper presents experiments with generative content models for analyzing the discourse structure of medical abstracts , which has been confirmed to follow the four-section pattern discussed above ( Salanger-Meyer , 1990 ) . Context after the citation: For a variety of reasons, medicine is an interesting domain of research. The need for information systems to support physicians at the point of care has been well studied (Covell et al., 1985; Gorman et al., 1994; Ely et al., 2005). Retrieval techniques can have a large impact on how physicians access and leverage clinical evidence. Information that satisfies physicians’ needs can be found in the MEDLINE database maintained by the U.S. National Library of Medicine

Background

acl_arc_intent_classification:train:1618

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Following Lekakos and Giaglis (2007), one approach for achieving this objective consists of applying supervised learning, where a winning method is selected for each case in the training set, all the training cases are labeled accordingly, and then the system is trained to predict a winner for unseen cases. These predictions enable our system to recommend a particular method for handling a new (unseen) request (Marom, Zukerman, and Japkowicz 2007). In other words, our meta-level process learns to predict the performance of the different methods from their confidence levels on the basis of previous experience. Citation Sentence: Following Lekakos and Giaglis ( 2007 ) , one approach for achieving this objective consists of applying supervised learning , where a winning method is selected for each case in the training set , all the training cases are labeled accordingly , and then the system is trained to predict a winner for unseen cases . Context after the citation: However, in our situation, there is not always one single winner (two methods can perform similarly well for a given request), and there are different ways to pick winners (for example, based on F-score or precision). Therefore, such an approach would require the utilization of subjective heuristics for creating labels, which would significantly influence what is being learned. Instead, we adopt an unsupervised approach that finds patterns in the data—confidence values coupled with performance scores (Section 6.1)—and then attempts to fit unseen data to these patterns (Section 6.2). Heuristics are still needed in order to decide which response-generation method to apply to an unseen case, but they are applied only after the learning is complete (Section 6.3).

CompareOrContrast

acl_arc_intent_classification:train:1619

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Fung and McKeown (1997) attempt to translate technical terms using word relation matrices, although the resource from which such relations are derived is a pair of nonparallel corpora. There is a wealth of literature on trying to establish subsentential translations from a bilingual corpus.3 Kay and R¨oscheisen (1993) attempt to extract a bilingual dictionary using a hybrid method of sentence and word alignment on the assumption that the (source, target) words have a similar distribution. All EBMT systems, from the initial proposal by Nagao (1984) to the recent collection of Carl and Way (2003), are premised on the availability of subsentential alignments derived from the input bitext. Citation Sentence: Fung and McKeown ( 1997 ) attempt to translate technical terms using word relation matrices , although the resource from which such relations are derived is a pair of nonparallel corpora . Context after the citation: Somers (1998) replicates the work of Fung and McKeown with different language pairs using the simpler metric of Levenshtein distance. Boutsis and Piperidis (1998) use a tagged parallel corpus to extract translationally equivalent English-Greek clauses on the basis of word occurrence and co-occurrence probabilities. The respective lengths of the putative alignments in terms of characters is also an important factor. Ahrenberg, Andersson, and Merkel (2002) observe that for less widely spoken languages, the relative lack of linguistic tools and resources has forced developers of word alignment tools for such languages to use shallow processing and basic statistical approaches to word linking.

Background

acl_arc_intent_classification:train:162

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Thus, over the past few years, along with advances in the use of learning and statistical methods for acquisition of full parsers (Collins, 1997; Charniak, 1997a; Charniak, 1997b; Ratnaparkhi, 1997), significant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship (Church, 1988; Ramshaw and Marcus, 1995; Argamon et al., 1998; Cardie and Pierce, 1998; Munoz et al., 1999; Punyakanok and Roth, 2001; Buchholz et al., 1999; Tjong Kim Sang and Buchholz, 2000). While earlier work in this direction concentrated on manual construction of rules, most of the recent work has been motivated by the observation that shallow syntactic information can be extracted using local information by examining the pattern itself, its nearby context and the local part-of-speech information. to ] [NP only $ 1.8 billion ] [PP in ] [NP September] . Citation Sentence: Thus , over the past few years , along with advances in the use of learning and statistical methods for acquisition of full parsers ( Collins , 1997 ; Charniak , 1997a ; Charniak , 1997b ; Ratnaparkhi , 1997 ) , significant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship ( Church , 1988 ; Ramshaw and Marcus , 1995 ; Argamon et al. , 1998 ; Cardie and Pierce , 1998 ; Munoz et al. , 1999 ; Punyakanok and Roth , 2001 ; Buchholz et al. , 1999 ; Tjong Kim Sang and Buchholz , 2000 ) . Context after the citation: Research on shallow parsing was inspired by psycholinguistics arguments (Gee and Grosjean, 1983) that suggest that in many scenarios (e.g., conversational) full parsing is not a realistic strategy for sentence processing and analysis, and was further motivated by several arguments from a natural language engineering viewpoint. First, it has been noted that in many natural language applications it is sufficient to use shallow parsing information; information such as noun phrases (NPs) and other syntactic sequences have been found useful in many large-scale language processing applications including information extraction and text summarization (Grishman, 1995; Appelt et al., 1993). Second, while training a full parser requires a collection of fully parsed sentences as training corpus, it is possible to train a shallow parser incrementally. If all that is available is a collection of sentences annotated for NPs, it can be used to produce this level of analysis.

Background

acl_arc_intent_classification:train:1620

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Due to their remarkable ability to incorporate context structure information and long distance reordering into the translation process, tree-based translation models have shown promising progress in improving translation quality (Liu et al., 2006, 2009; Quirk et al., 2005; Galley et al., 2004, 2006; Marcu et al., 2006; Shen et al., 2008; Zhang et al., 2011b). In recent years, tree-based translation models1 are drawing more and more attention in the community of statistical machine translation (SMT). Citation Sentence: Due to their remarkable ability to incorporate context structure information and long distance reordering into the translation process , tree-based translation models have shown promising progress in improving translation quality ( Liu et al. , 2006 , 2009 ; Quirk et al. , 2005 ; Galley et al. , 2004 , 2006 ; Marcu et al. , 2006 ; Shen et al. , 2008 ; Zhang et al. , 2011b ) . Context after the citation: However, tree-based translation models always suffer from two major challenges: 1) They are usually built directly from parse trees, which are generated by supervised linguistic parsers. 1 A tree-based translation model is defined as a model using tree structures on one side or both sides. However, for many language pairs, it is difficult to acquire such corresponding linguistic parsers due to the lack of Tree-bank resources for training. 2) Parse trees are actually only used to model and explain the monolingual structure, rather than the bilingual mapping between language pairs.

Background

acl_arc_intent_classification:train:1621

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: 11 Nivre (2008) reports that non-projective and pseudo-projective algorithms outperform the “eager” projective algorithm in MaltParser, but our training data did not contain any non-projective dependencies. To recap, it has the following MaltParser attributes (machine learning features): 4 word-form attributes, 7 POS tag attributes, and 5 deprel attributes (some of which are not useful for the Nivre “eager” algorithm), totaling 16 attributes and two more for every new feature described in Section 4.3 and on (e.g., CASE). All experiments reported here were conducted using this new configuration. Citation Sentence: 11 Nivre ( 2008 ) reports that non-projective and pseudo-projective algorithms outperform the `` eager '' projective algorithm in MaltParser , but our training data did not contain any non-projective dependencies . Context after the citation: The Nivre “standard” algorithm is also reported there to do better on Arabic, but in a preliminary experimentation, it did slightly worse than the “eager” one, perhaps due to the high percentage of right branching (left headed structures) in our Arabic training set—an observation already noted in Nivre (2008). 12 The terms feature and attribute are overloaded in the literature. We use them in the linguistic sense, unless specifically noted otherwise, e.g., MaltParser feature(s). 13 It is slightly different from the default configuration.

CompareOrContrast

acl_arc_intent_classification:train:1622

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: In particular, boosting (Schapire, 1999; Abney et al., 1999) offers the possibility of achieving high accuracy from a collection of classifiers which individually perform quite poorly. It would be worthwhile to investigate applying some of the more sophisticated ensemble learning techniques which have been proposed in the literature (Dietterich, 1997). The technique method described in section 3.7 is a fairly crude method for combining frequency information with symbolic data. Citation Sentence: In particular , boosting ( Schapire , 1999 ; Abney et al. , 1999 ) offers the possibility of achieving high accuracy from a collection of classifiers which individually perform quite poorly . Context after the citation:

FutureWork

acl_arc_intent_classification:train:1623

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For MT the most commonly used heuristic is called grow diagonal final (Och and Ney 2003). alignment. Typically this is achieved by a symmetrization heuristic that takes two directional alignments and produces a single Citation Sentence: For MT the most commonly used heuristic is called grow diagonal final ( Och and Ney 2003 ) . Context after the citation: This starts with the intersection of the sets of aligned points and adds points around the diagonal that are in the union of the two sets of aligned points. The alignment produced has high recall relative to the intersection and only slightly lower recall than the union. In syntax transfer the intersection heuristic is normally used, because one wants to have high precision links to transfer knowledge between languages. One pitfall of these symmetrization heuristics is that they can obfuscate the link between the original alignment and the ones used for a specific task, making errors more difficult to analyze.

CompareOrContrast

acl_arc_intent_classification:train:1624

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: ECM-F is an entity-constrained mention Fmeasure (cfXXX (Luo et al., 2004) for how ECM-F is computed), and ACE-Value is the official ACE evaluation metric. We report results with two metrics: ECM-F and ACEValue. “True” mentions mean that input to the coreference system are mentions marked by human, while system mentions are output from the mention detection system. Citation Sentence: ECM-F is an entity-constrained mention Fmeasure ( cfXXX ( Luo et al. , 2004 ) for how ECM-F is computed ) , and ACE-Value is the official ACE evaluation metric . Context after the citation: The result is shown in Table 4: the baseline numbers without stem features are listed under “Base,” and the results of the coreference system with stem features are listed under “Base+Stem.&quot; On true mention, the stem matching features improve ECM-F from 77.7% to 80.0%, and ACE-value from 86.9% to 88.2%. The similar improvement is also observed on system mentions.The overall ECMF improves from 62.3% to 64.2% and the ACE value improves from 61.9 to 63.1%. Note that the increase on the ACE value is smaller than ECM-F.

Uses

acl_arc_intent_classification:train:1625

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: In order to obtain semantic representations of each word, we apply our previous strategy (Schone and Jurafsky (2000)). This methodology is well-described in the literature (Landauer, et al., 1998; Manning and Schütze, 1999). The LSA approach then zeros out all but the top k singular values of the SVD, which has the effect of projecting vectors into an optimal kdimensional subspace. Citation Sentence: In order to obtain semantic representations of each word , we apply our previous strategy ( Schone and Jurafsky ( 2000 ) ) . Context after the citation: Rather than using a termdocument matrix, we had followed an approach akin to that of Schütze (1993), who performed SVD on a Nx2N term-term matrix. The N here represents the N-1 most-frequent words as well as a glob position to account for all other words not in the top N-1. The matrix is structured such that for a given word w’s row, the first N columns denote words that precede w by up to 50 words, and the second N � columns represent those words that follow by up to NCS(µ, ) =f NCS exp[ ((x-µ)/ )2]dx 50 words. Since SVDs are more designed to work then, if there were nR items in the ruleset, the with normally-distributed data (Manning and probability that a NCS is non-random is Schütze, 1999, p. 565), we fill each entry with a Pr(NCS)_ nT NCS(µT, T) normalized count (or Z-score) rather than straight frequency.

Extends

acl_arc_intent_classification:train:1626

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Finkelstein et al. (2002) annotated a larger set of word pairs (353), too. This setup is also scalable to a higher number of word pairs (350) as was shown in Gurevych (2006). She used an adapted experimental setup where test subjects had to assign discrete values {0,1,2,3,4} and word pairs were presented in isolation. Citation Sentence: Finkelstein et al. ( 2002 ) annotated a larger set of word pairs ( 353 ) , too . Context after the citation: They used a 0-10 range of relatedness scores, but did not give further details about their experimental setup. In psycholinguistics, relatedness of words can also be determined through association tests (Schulte im Walde and Melinger, 2005). Results of such experiments are hard to quantify and cannot easily serve as the basis for evaluating SR measures. Rubenstein and Goodenough selected word pairs analytically to cover the whole spectrum of

Background

acl_arc_intent_classification:train:1627