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It can be shown ( Berger et al. , 1996 ) that the use of this model with maximum likelihood parameter estimation is justified on information-theoretic grounds when q represents some prior knowledge about the true distribution and when the expected values of f in the training corpus are identical to their true expected values .3 There is no requirement that the components of f represent disjoint or statistically independent events .
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Shortly after the publication of The Sound Pattern of English ( Chomsky and Halle 1968 ) , Kornai points out , `` Johnson ( 1970 ) demonstrated that the context-sensitive machinery of SPE ... [ could ] be replaced by a much simpler one , based on finite-state transducers ( FSTs ) ; the same conclusion was reached independently by Kaplan and Kay , whose work remained an underground classic until it was finally published in Kaplan and Kay ( 1994 ) . ''
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18 In this article , we use a newer version of the corpus by Alkuhlani and Habash ( 2011 ) than the one we used in Marton , Habash , and Rambow ( 2011 ) .
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Atallah et al. ( 2001b ) and Topkara et al. ( 2006a ) attained the embedding capacity of 0.5 bits per sentence with the syntactic transformation method .
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Another possibility that often works better is to use Minimum Bayes-Risk ( MBR ) decoding ( Kumar and Byrne 2002 ; Liang , Taskar , and Klein 2006 ; Ganchev , and Taskar 2007 ) .
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The subcategorization requirements expressed by semantic forms are enforced at f-structure level through completeness and coherence well-formedness conditions on f-structure ( Kaplan and Bresnan 1982 ) : An f-structure is locally complete iff it contains all the governable grammatical functions that its predicate governs .
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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 .
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Following the example of Landow ( 1994 ) , we will call the autonomous units of a hypertext lexias ( from ` lexicon ' ) , a word coined by Roland Barthes ( 1970 ) .
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( Chomsky 1977 ) .
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We use the same set of binary features as in previous work on this dataset ( Pang et al. , 2002 ; Pang and Lee , 2004 ; Zaidan et al. , 2007 ) .
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Various feature selection techniques have been used in automatic text categorization ; they include document frequency ( DF ) , information gain ( IG ) ( Tzeras and Hartman , 1993 ) , minimum description length principal ( Lang , 1995 ) , and the X2 statistic .
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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 ) .
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Petrov et al. ( 2010 ) observed that dependency parsers tend to do quite poorly when parsing questions due to their limited exposure to them in the news corpora from the PennTreebank .
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Lin ( 1998a ) 's similar word list for eat misses these but includes sleep ( ranked 6 ) and sit ( ranked 14 ) , because these have similar subjects to eat .
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• Learnability ( Zernik and Dyer 1987 ) • Text generation ( Hovy 1988 ; Milosavljevic , Tulloch , and Dale 1996 ) • Speech generation ( Rayner and Carter 1997 ) • Localization ( Sch ¨ aler 1996 )
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For example , a ` web page ' is more similar to an infinite canvas than a written page ( McCloud , 2001 ) .
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Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary , i.e. , mouse Mouse Genome Database ( MGD ) [ 18 ] , fly FlyBase [ 19 ] , yeast Saccharomyces Genome Database ( SGD ) [ 20 ] , rat -- Rat Genome Database ( RGD ) [ 21 ] , worm -- WormBase [ 22 ] , Human Nomenclature Database ( HUGO ) [ 23 ] , Online Mendelian Inheritance in Man ( OMIM ) [ 24 ] , and Enzyme Nomenclature Database ( ECNUM ) [ 25 , 26 ] .
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2This view of typed feature structures differs from the perspective on typed feature structures as modeling partial information as in ( Carpenter , 1992 ) .
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This approach resembles the work by Grishman et al. ( 1986 ) and Hirschman et al. ( 1975 ) on selectional restrictions .
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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 .
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LTAG ( Schabes et al. , 1988 ) is a grammar formalism that provides syntactic analyses for a sentence by composing elementary trees with two opera -
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Translations have been generated by the CrowdFlower3 channel to Amazon Mechanical Turk4 ( MTurk ) , adopting the methodology proposed by ( Negri and Mehdad , 2010 ) .
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This work is a continuation of that initiated in ( Yahyaoui , 2001 ) , which reports an overall NB classification correctness of 75.6 % , in cross validation experiments , on a data set that consists of 100 documents for each of 12 categories ( the data set is collected from different Arabic portals ) .
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Our previous work ( Zhai et al. , 2012 ) designed an EMbased method to construct unsupervised trees for tree-based translation models .
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Then , we binarize the English parse trees using the head binarization approach ( Wang et al. , 2007 ) and use the resulting binary parse trees to build another s2t system .
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This is the strongest version of the sorites paradox ( e.g. , Hyde 2002 ) .
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We used a standard implementation of IBM Model 4 ( Och and Ney 2003 ) and because changing the existing code is not trivial , we could not use the same stopping criterion to avoid overfitting and we are not able to produce precision/recall curves .
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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 .
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Some approaches apply semantic parsing , where words and sentences are mapped to logical structure meaning ( Kate and Mooney , 2007 ) .
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For projective parsing , it is significantly faster than exact dynamic programming , at the cost of small amounts of search error , We are interested in extending these ideas to phrase-structure and lattice parsing , and in trying other higher-order features , such as those used in parse reranking ( Charniak and Johnson , 2005 ; Huang , 2008 ) and history-based parsing ( Nivre and McDonald , 2008 ) .
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Prototypes of Internet search engines for linguists , corpus linguists and lexicographers have been proposed : WebCorp ( Kehoe and Renouf , 2002 ) , KWiCFinder ( Fletcher , 2004a ) and the Linguist 's Search Engine ( Kilgarriff , 2003 ; Resnik and Elkiss , 2003 ) .
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Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary , i.e. , mouse Mouse Genome Database ( MGD ) [ 18 ] , fly FlyBase [ 19 ] , yeast Saccharomyces Genome Database ( SGD ) [ 20 ] , rat -- Rat Genome Database ( RGD ) [ 21 ] , worm -- WormBase [ 22 ] , Human Nomenclature Database ( HUGO ) [ 23 ] , Online Mendelian Inheritance in Man ( OMIM ) [ 24 ] , and Enzyme Nomenclature Database ( ECNUM ) [ 25 , 26 ] .
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Other factors , such as the role of focus ( Grosz 1977 , 1978 ; Sidner 1983 ) or quantifier scoping ( Webber 1983 ) must play a role , too .
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Since mid-2002 , the Library has been employing software that automatically suggests MeSH headings based on content ( Aronson et al. 2004 ) .
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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 ) .
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Also , the Keller and Lapata ( 2003 ) approach will be undefined if the pair is unobserved on the web .
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The automation of help-desk responses has been previously tackled using mainly knowledge-intensive paradigms , such as expert systems ( Barr and Tessler 1995 ) and case-based reasoning ( Watson 1997 ) .
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We measure this association using pointwise Mutual Information ( MI ) ( Church and Hanks , 1990 ) .
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Some methods of semantic relation analysis rely on predefined templates filled with information from processed texts ( Baker et al. , 1998 ) .
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There are two corpora normally used for evaluation in a number of text-processing tasks : the Brown corpus ( Francis and Kucera 1982 ) and the Wall Street Journal ( WSJ ) corpus , both part of the Penn Treebank ( Marcus , Marcinkiewicz , and Santorini 1993 ) .
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or quotation of messages in emails or postings ( see Mullen and Malouf ( 2006 ) but cfXXX Agrawal et al. ( 2003 ) ) .
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Andrews et al. ( 2009 ) extend LDA to allow for the inference of document and topic distributions in a multimodal corpus .
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SNoW ( Carleson et al. , 1999 ; Roth , 1998 ) is a multi-class classifier that is specifically tailored for learning in domains in which the potential number of information sources ( features ) taking part in decisions is very large , of which NLP is a principal example .
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The search algorithm is the standard Viterbi search ( Viterbi 1967 ) , except that the match involves a network-to-network alignment problem rather than sequence-to-sequence .
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Since then this idea has been applied to several tasks , including word sense disambiguation ( Yarowsky 1995 ) and named-entity recognition ( Cucerzan and Yarowsky 1999 ) .
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7 We employed the LIBSVM package ( Chang and Lin 2001 ) .
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coreference performance on perfect mentions ( e.g. , Incorporate the two knowledge sources in a Luo et al. ( 2004 ) ) ; and for those that do report percoreference resolver .
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According to Hobbs ( 1979 , p. 67 ) , these two sentences are incoherent .
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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 ) .
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Another dialogue acquisition system has been developed by Ho ( 1984 ) .
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Lexical functional grammar ( Kaplan and Bresnan 1982 ; Bresnan 2001 ; Dalrymple 2001 ) is a member of the family of constraint-based grammars .
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1Our rules are similar to those from Xu et al. ( 2009 ) .
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In psycholinguistics , relatedness of words can also be determined through association tests ( Schulte im Walde and Melinger , 2005 ) .
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In this article , we use an in-house system which provides functional gender , number , and rationality features ( Alkuhlani and Habash 2012 ) .
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Over the last decade there has been a lot of interest in developing tutorial dialogue systems that understand student explanations ( Jordan et al. , 2006 ; Graesser et al. , 1999 ; Aleven et al. , 2001 ; Buckley and Wolska , 2007 ; Nielsen et al. , 2008 ; VanLehn et al. , 2007 ) , because high percentages of selfexplanation and student contentful talk are known to be correlated with better learning in humanhuman tutoring ( Chi et al. , 1994 ; Litman et al. , 2009 ; Purandare and Litman , 2008 ; Steinhauser et al. , 2007 ) .
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The use of the web as a corpus for teaching and research on language has been proposed a number of times ( Kilgarriff , 2001 ; Robb , 2003 ; Rundell , 2000 ; Fletcher , 2001 , 2004b ) and received a special issue of the journal Computational Linguistics ( Kilgarriff and Grefenstette , 2003 ) .
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Several authors in communication studies have pointed out that head movements are relevant to feedback phenomena ( see McClave ( 2000 ) for an overview ) .
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Our method resorts to some translation examples , which is similar as example-based translation or translation memory ( Watanabe and Sumita , 2003 ; He et al. , 2010 ; Ma et al. , 2011 ) .
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Baseline Systems We choose three publicly available state-of-the-art end-to-end coreference systems as our baselines : Stanford system ( Lee et al. , 2011 ) , Berkeley system ( Durrett and Klein , 2014 ) and HOTCoref system ( Bj ¨ orkelund and Kuhn , 2014 ) .
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A number of speech understanding systems have been developed during the past fifteen years ( Barnett et al. 1980 , Dixon and Martin 1979 , Erman et al. 1980 , Haton and Pierrel 1976 , Lea 1980 , Lowerre and Reddy 1980 , Medress 1980 , Reddy 1976 , Walker 1978 , and Wolf and Woods 1980 ) .
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As for work on Arabic ( MSA ) , results have been reported on the PATB ( Kulick , Gabbard , and Marcus 2006 ; Diab 2007 ; Green and Manning 2010 ) , the Prague Dependency Treebank ( PADT ) ( Buchholz and Marsi 2006 ; Nivre 2008 ) and the CATiB ( Habash and Roth 2009 ) .
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Other representations use the link structure ( Malin , 2005 ) or generate graph representations of the extracted features ( Kalashnikov et al. , 2007 ) .
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From an IR view , a lot of specialized research has already been carried out for medical applications , with emphasis on the lexico-semantic aspects of dederivation and decomposition ( Pacak et al. , 1980 ; Norton and Pacak , 1983 ; Wolff , 1984 ; Wingert , 1985 ; Dujols et al. , 1991 ; Baud et al. , 1998 ) .
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There has been some controversy , at least for simple stemmers ( Lovins , 1968 ; Porter , 1980 ) , about the effectiveness of morphological analysis for document retrieval ( Harman , 1991 ; Krovetz , 1993 ; Hull , 1996 ) .
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This revalidates the observation of Nguyen et al. ( 2009 ) that phrase structure representations and dependency representations add complimentary value to the learning task .
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The parsing algorithm used for all languages is the deterministic algorithm first proposed for unlabeled dependency parsing by Nivre ( 2003 ) and extended to labeled dependency parsing by Nivre et al. ( 2004 ) .
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The semantic categories of verbs and other words are extracted from the Semantic Knowledge-base of Contemporary Chinese ( Wang et al. 2003 ) .
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Moreover , in order to determine whether the performances of the predictive criteria are consistent across different learning models within the same domain , we have performed the study on two parsing models : one based on a context-free variant of tree-adjoining grammars ( Joshi , Levy , and Takahashi 1975 ) , the Probabilistic Lexicalized Tree Insertion Grammar ( PLTIG ) formalism ( Schabes and Waters 1993 ; Hwa 1998 ) , and Collins 's Model 2 parser ( 1997 ) .
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6 The Partial-VP Topicalization Lexical Rule proposed by Hinrichs and Nakazawa ( 1994 , 10 ) is a linguistic example .
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We measure the inter annotator agreement using the Fleiss Kappa ( Fleiss et al. , 1981 ) measure ( x ) where the agreement lies around 0.79 .
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The task we used to compare different generalisation techniques is similar to that used by Pereira et al. ( 1993 ) and Rooth et al. ( 1999 ) .
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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 ) .
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It is not aimed at handling dependencies , which require heavy use of lexical information ( Hindle and Rooth , 1993 , for PP attachment ) .
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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 .
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I A more detailed discussion of various aspects of the proposed parser can be found in ( Minnen , 1998 ) .
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Our experimental design with professional bilingual translators follows our previous work Green et al. ( 2013a ) comparing scratch translation to post-edit .
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Lexical functional grammar ( Kaplan and Bresnan 1982 ; Bresnan 2001 ; Dalrymple 2001 ) is a member of the family of constraint-based grammars .
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Thus , the second class of SBD systems employs machine learning techniques such as decision tree classifiers ( Riley 1989 ) , neural networks ( Palmer and Hearst 1994 ) , and maximum-entropy modeling ( Reynar and Ratnaparkhi 1997 ) .
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Previously , a user study ( Lin et al. 2003 ) has shown that people are reluctant to type full natural language questions , even after being told that they were using a questionanswering system and that typing complete questions would result in better performance .
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We will examine the worst-case complexity of interpretation as well as generation to shed some light on the hypothesis that vague descriptions are more difficult to process than others because they involve a comparison between objects ( Beun and Cremers 1998 , Krahmer and Theune 2002 ) .
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In our case , the clustering is performed by the program Snob , which implements mixture modeling combined with model selection based on the Minimum Message Length ( MML ) criterion ( Wallace and Boulton 1968 ; Wallace 2005 ) .
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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 ) .
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Many provide graphical user interfaces ( GUI ) for manual annotation ( e.g. General Architecture for Text Engineering ( GATE ) ( Cunningham et al. , 1997 ) and the Alembic Workbench ( Day et al. , 1997 ) ) as well as NLP tools and resources that can be manipulated from the GUI .
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The keypoints are clustered into 5,000 visual codewords ( centroids ) using k-means clustering ( Sculley , 2010 ) , and images are then quantized over the 5,000 codewords .
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Another approach for partial parsing was presented by Skut and Brants ( 1998 ) .
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In knowledge-lean approaches , coreference resolvers employ only morpho-syntactic cues as knowledge sources in the resolution process ( e.g. , Mitkov ( 1998 ) , Tetreault ( 2001 ) ) .
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The application of domain models and deep semantic knowledge to question answering has been explored by a variety of researchers ( e.g. , Jacquemart and Zweigenbaum 2003 , Rinaldi et al. 2004 ) , and was also the focus of recent workshops on question answering in restricted domains at ACL 2004 and AAAI 2005 .
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This result is consistent with other works using this model with these features ( Andrews et al. , 2009 ; Silberer and Lapata , 2012 ) .
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Cases of intracategorial synonymy are relatively straigthtforward as several electronic synonym dictionnaries for french are available ( Ploux , 1997 ) .
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The feature of head word trigger which we apply to the log-linear model is motivated by the trigger-based approach ( Hasan and Ney , 2009 ) .
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Other work on modeling the meanings of verbs using video recognition has also begun showing great promise ( Mathe et al. , 2008 ; Regneri et al. , 2013 ) .
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based parsing algorithms with an arc-factored parameterization ( McDonald et al. , 2005 ) .
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No attempt has been made to map any closed class entries from LDOCE , as a 3,000 word lexicon containing most closed class items has been developed independently by one of the groups collaborating with us to develop the general purpose morphological and syntactic analyser ( see the Introduction and Russell et al. , 1986 ) .
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In this paper we focus on the exploitation of the LDOCE grammar coding system ; Alshawi et al. ( 1985 ) and Alshawi ( 1987 ) describe further research in Cambridge utilising different types of information available in LDOCE .
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Thus , the second class of SBD systems employs machine learning techniques such as decision tree classifiers ( Riley 1989 ) , neural networks ( Palmer and Hearst 1994 ) , and maximum-entropy modeling ( Reynar and Ratnaparkhi 1997 ) .
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We see no good reason , however , why such text spans should necessarily be sentences , since the majority of tagging paradigms ( e.g. , Hidden Markov Model [ HMM ] [ Kupiec 1992 ] , Brill 's [ Brill 1995a ] , and MaxEnt [ Ratnaparkhi 1996 ] ) do not attempt to parse an entire sentence and operate only in the local window of two to three tokens .
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Similarly , ( Barzilay and Lee , 2003 ) and ( Shinyanma et al. , 2002 ) learn sentence level paraphrase templates from a corpus of news articles stemming from different news source .
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In future work we plan to experiment with richer representations , e.g. including long-range n-grams ( Rosenfeld , 1996 ) , class n-grams ( Brown et al. , 1992 ) , grammatical features ( Amaya and Benedy , 2001 ) , etc ' .
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Furthermore , we demonstrate that our results carry over successfully to another parser , the Easy-First Parser ( Goldberg and Elhadad 2010 ) ( Section 6 ) .
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For instance , relating `` they '' to `` apples '' in the sentence ( cfXXX Haugeland 1985 p. 195 ; Zadrozny 1987a ) : We bought the boys apples because they were so cheap
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