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we train a 4-gram language model on the xinhua portion of the gigaword corpus using the sri language toolkit with modified kneser-ney smoothing . | we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model . |
compared with n-gram models , syntactic models give overall better performance . | syntactic models give better performance compared with n-gram models , despite trained with less data . |
the rhetorical structure theory is a language independent theory based on the idea that a text can be segmented into elementary discourse units linked by means of nucleus-satellite or multinuclear rhetorical relations . | rhetorical structure theory is a well known text representation technique that represents the knowledge present in the text using semantic relations known as discourse relations . |
we used smoothed bleu for benchmarking purposes . | therefore , we used bleu and rouge as automatic evaluation measures . |
we used the phrasebased translation system in moses 5 as a baseline smt system . | for training the translation model and for decoding we used the moses toolkit . |
in our paper , we use te to compute connectivity between nodes of the graph and apply the weighted minimum vertex cover ( w mvc ) algorithm on the graph . | in our paper , we use te to compute connectivity between nodes of the graph and apply the weighted minimum vertex cover ( w mvc ) algorithm on the graph to select the sentences for the summary . |
aspect extraction is a key task of opinion mining ( cite-p-15-1-14 ) . | aspect extraction is a central problem in sentiment analysis . |
by cite-p-8-1-4 , recent attempts that apply either complex linguistic reasoning or attention-based complex neural network architectures achieve up to 76 % accuracy on benchmark sets . | while this does not seem like a challenging task , many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65 % ¨c76 % accuracy on benchmark sets . |
li et al replaced oovs with in-vocabulary words by semantic similarity to reduce the negative effect for words around the oovs . | li et al substituted oov words in training corpora with a similar in-vocabulary word as pre-and post-processing steps . |
we tested our methods on the english penn treebank . | we used small portions of the penn wsj treebank for the experiments . |
for instance , bengio et al present a neural probabilistic language model that uses the n-gram model to learn word embeddings . | bengio et al presented a neural network language model where word embeddings are simultaneously learned along with a language model . |
examples of topic models include plsi and lda . | one of the most popular and well-known topic models is lda . |
lin and he propose a joint topic-sentiment model , but topic words and sentiment words are still not explicitly separated . | lin and he propose a method based on lda that explicitly deals with the interaction of topics and sentiments in text . |
our baseline is a standard phrase-based smt system . | our direct system uses the phrase-based translation system . |
word sense disambiguation ( wsd ) is a key task in computational lexical semantics , inasmuch as it addresses the lexical ambiguity of text by making explicit the meaning of words occurring in a given context ( cite-p-18-3-10 ) . | word sense disambiguation ( wsd ) is the task to identify the intended sense of a word in a computational manner based on the context in which it appears ( cite-p-13-3-4 ) . |
given the model parameters and a sentence pair math-w-2-14-1-11 , compute math-w-2-14-1-18 . | given the parameters of ibm model 3 , and a sentence pair math-w-5-1-0-21 , compute the probability math-w-5-1-0-30 . |
kamp is a multiple-agent planning system designed around a noah-like hierarchical planner [ 10 ] . | kamp is a multiagent planning system that can be given a high-level description of an agent 's goals , and then produce a plan that includes the performance of both physical and linguistic actions by several agents that will achieve the agent 's goals . |
the bilda model is a straightforward multilingual extension of the standard lda model . | we compare our graphbtm approach with the avitm and the lda model . |
to that end , we take the classification algorithm outlined earlier in section 4 , and apply it to the switchboard corpus for both training and testing , replicating the work reported in webb et al . | first , to establish our baseline tagging performance , we take the classification algorithm outlined earlier in section 4 , and apply it to the switchboard corpus for both training and testing , replicating the work reported in webb et al . |
we then lowercase all data and use all sentences from the modern dutch part of the corpus to train an n-gram language model with the srilm toolkit . | for improving the word alignment , we use the word-classes that are trained from a monolingual corpus using the srilm toolkit . |
can be evaluated by maximizing the pseudo-likelihood on a training corpus , . | can be evaluated by maximising the pseudo-likelihood on a training corpus . |
relation extraction is a challenging task in natural language processing . | relation extraction is a traditional information extraction task which aims at detecting and classifying semantic relations between entities in text ( cite-p-10-1-18 ) . |
entity linking ( el ) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions ( persons , organizations , etc ) . | entity linking ( el ) is the task of automatically linking mentions of entities ( e.g . persons , locations , organizations ) in a text to their corresponding entry in a given knowledge base ( kb ) , such as wikipedia or freebase . |
phrase reordering is a common problem when translating between two grammatically different languages . | phrase reordering is a challenge for statistical machine translation systems . |
semantic role labeling ( srl ) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence . | semantic role labeling ( srl ) has been defined as a sentence-level natural-language processing task in which semantic roles are assigned to the syntactic arguments of a predicate ( cite-p-14-1-7 ) . |
in the reranking stage is performed using linear interpolation of these models . | in the reranking stage , we use linearly combined model of these models . |
relation extraction ( re ) is a task of identifying typed relations between known entity mentions in a sentence . | relation extraction is a challenging task in natural language processing . |
our trigram word language model was trained on the target side of the training corpus using the srilm toolkit with modified kneser-ney smoothing . | a 4-gram language model which was trained on the entire training corpus using srilm was used to generate responses in conjunction with the phrase-based translation model . |
we used a phrase-based smt model as implemented in the moses toolkit . | we use phrase based moses with default options as the spe engine . |
abbasi et al applies sentiment analysis techniques to identify and classify documentlevel opinions in text crawled from english and arabic web forums . | abbasi et al use a genetic algorithm for both english and arabic web forums sentiment detection on the document level . |
for the classification task , we use pre-trained glove embedding vectors as lexical features . | the statistics for these datasets are summarized in settings we use glove vectors with 840b tokens as the pre-trained word embeddings . |
cao et al explained topic models from the perspective of neural networks and proposed a neural topic model where the representation of words and documents are combined into a unified framework . | cao et al , 2015 ) proposed a novel neural topic model where the representation of words and documents are efficiently and naturally combined into a uniform framework . |
the model weights are automatically tuned using minimum error rate training . | the feature weights are tuned with mert to maximize bleu-4 . |
we created a data collection for research , development and evaluation of a method for automatically answering why-questions ( why-qa ) . | we created a data collection for research into why-questions and for development of a method for why-qa . |
an idiom is a relatively frozen expression whose meaning can not be built compositionally from the meanings of its component words . | an idiom is a combination of words that has a figurative meaning which differs from its literal meaning . |
for all three classifiers , we used the word2vec 300d pre-trained embeddings as features . | as embedding vectors , we used the publicly available representations obtained from the word2vec cbow model . |
sentiment analysis is a recent attempt to deal with evaluative aspects of text . | one of the first challenges in sentiment analysis is the vast lexical diversity of subjective language . |
all language models are created with the srilm toolkit and are standard 4-gram lms with interpolated modified kneser-ney smoothing . | language models were built with srilm , modified kneser-ney smoothing , default pruning , and order 5 . |
in a preprocessing step , we apply the coreference resolution module of stanford corenlp to the whole corpus . | first , we extract the named entities in the text using stanford corenlp . |
we evaluated the performance of the three pruning criteria in a real application of chinese text input . | we evaluated the performance of the three pruning criteria in a real application of chinese text input ( cite-p-15-1-2 ) through cer . |
nlp researchers have the potential to significantly advance gun violence research . | these nlp tools have the potential to make a marked difference for gun violence researchers . |
the backbone of our system is a character-based segmenter with the application of crf that provides a framework to use a large number of linguistic features . | the backbone of our system is a character-based segmenter with the application of conditional random fields . |
but the development of cohesion-based unsupervised methods is an interesting possibility for future work . | the development of more comprehensive bayesian models for discourse structure seems an exciting direction for future research . |
in this paper , we develop an approach based on recurrent neural networks . | we have explored a variety of neural network models in this paper to identify the most suitable model . |
user : i want to prevent tom from reading my file . | user : so i have to remove a file to create a file ? |
word embeddings are commonly estimated from large text corpora utilizing statistics concerning the co-occurrences of words . | word embedding models are aimed at learning vector representations of word meaning . |
the candidate answer with the highest probability will be selected as the target . | the candidate with the highest probability was chosen as the target entity . |
for the fluency and grammaticality features , we train 4-gram lms using the development dataset with the sri toolkit . | we train a 4-gram language model on the xinhua portion of english gigaword corpus by srilm toolkit . |
in core task , using 6 types of similarity measures , i . e . , string similarity , number similarity , knowledge-based similarity , corpus-based similarity , syntactic dependency similarity and machine translation similarity . | for core task , we collect 6 types of similarity measures , i.e. , string similarity , number similarity , knowledge-based similarity , corpus-based similarity , syntactic dependency similarity and machine translation similarity . |
the restaurants dataset contains 3,710 english sentences from the reviews of ganu et al . | the restaurants dataset contains 3,710 english sentences from the restaurant reviews of ganu et al . |
we used the logistic regression implementation in scikit-learn for the maximum entropy models in our experiments . | we use the logistic regression implementation of liblinear wrapped by the scikit-learn library . |
abstract meaning representation is a popular framework for annotating whole sentence meaning . | the abstract meaning representation is a semantic meaning representation language that is purposefully syntax-agnostic . |
lstm and gru networks are known to be successful remedies to these problems . | the lstm architecture is proposed to address this problem . |
we use srilm to train a 5-gram language model on the target side of our training corpus with modified kneser-ney discounting . | we used the sri language modeling toolkit to train lms on our training data for each ilr level . |
main tasks include aspect extraction , opinion polarity identification and subjectivity analysis . | main tasks include aspect extraction , polarity identification and subjectivity analysis . |
we ’ ve demonstrated that the benefits of unsupervised multilingual learning increase steadily with the number of available languages . | our experiments show that performance improves steadily as the number of languages increases . |
probabilistic context-free grammars are commonly used in parsing and grammar induction systems . | probabilistic context-free grammars underlie most high-performance parsers in one way or another . |
coreference resolution is the task of clustering a set of mentions in the text such that all mentions in the same cluster refer to the same entity . | coreference resolution is a well known clustering task in natural language processing . |
since our dataset is not so large , we make use of pre-trained word embeddings , which are trained on a much larger corpus with word2vec toolkit . | due to their ability to capture syntactic and semantic information of words from large scale unlabeled texts , we pre-train the word embeddings from the given training dataset by word2vec toolkit . |
the various smt systems are evaluated using the bleu score . | the various models developed are evaluated using bleu and nist . |
coreference resolution is the task of determining when two textual mentions name the same individual . | coreference resolution is the task of clustering referring expressions in a text so that each resulting cluster represents an entity . |
gamon shows that svm with deep linguistic features can further improve the performance . | gamon shows that introducing deeper linguistic features into svm can help to improve the performance . |
twitter is a huge microblogging service with more than 500 million tweets per day from different locations of the world and in different languages ( cite-p-10-1-6 ) . | twitter is a fantastic data resource for many tasks : measuring political ( o ’ connor et al. , 2010 ; tumasjan et al. , 2010 ) , and general sentiment ( cite-p-11-1-3 ) , studying linguistic variation ( cite-p-11-3-2 ) and detecting earthquakes ( cite-p-11-3-18 ) . |
ccg is a linguistically-motivated categorial formalism for modeling a wide range of language phenomena . | ccg is a linguistic formalism that tightly couples syntax and semantic . |
the system output is evaluated using the meteor and bleu scores computed against a single reference sentence . | to evaluate the full abstract generation system , the bleu score is computed with human abstracts as reference . |
teufel and moens , 2002 ) introduced az and applied it first to computational linguistics papers . | teufel and moens introduced az and applied it to computational linguistics papers . |
by including age or gender information , we consistently and significantly improve performance over demographic-agnostic models . | we find that demographic-aware models consistently outperform their agnostic counterparts in all tasks . |
we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit . | firstly , we built a forward 5-gram language model using the srilm toolkit with modified kneser-ney smoothing . |
relation extraction ( re ) is a task of identifying typed relations between known entity mentions in a sentence . | relation extraction is the task of predicting attributes and relations for entities in a sentence ( zelenko et al. , 2003 ; bunescu and mooney , 2005 ; guodong et al. , 2005 ) . |
potash et al use an encoder-decoder problem formulation by employing a pointer network based deep neural network architecture . | potash et al proposed a neural architecture based on a pointer network for jointly predicting types of argumentative components and identifying argumentative relations . |
discourse parsing is a difficult , multifaceted problem involving the understanding and modeling of various semantic and pragmatic phenomena as well as understanding the structural properties that a discourse graph can have . | discourse parsing is the task of identifying the presence and the type of the discourse relations between discourse units . |
statistical machine translation methods are well established in speech-to-speech translation systems as the main translation technique . | concept classification , as an alternative translation method , has been successfully integrated in speech-to-speech translators . |
smith et al proposed a log-linear model for the context-based disambiguation of a morphological dictionary . | smith et al used a conditional random field to learn to disambiguate over sentence by modeling local contexts . |
we used the moses mt toolkit with default settings and features for both phrase-based and hierarchical systems . | we used the moses machine translation decoder , using the default features and decoding settings . |
using appropriate word weighting functions is known to improve the performance of text categorization . | the use of word unigrams is a standard approach in text classification , and has also been successfully used to predict reading difficulty . |
we consider a phrase-based translation model and a hierarchical translation model . | the target-normalized hierarchical phrase-based model is based on a more general hierarchical phrase-based model . |
to learn the user-dependent word embeddings for stance classification and visualization , we train the 50-dimensional word embeddings via glove . | we use glove vectors with 200 dimensions as pre-trained word embeddings , which are tuned during training . |
after harvesting axioms from textbooks , we also present an approach to parse the axiom mentions to horn clause rules . | in this paper , we present an approach to obtain axiomatic knowledge of geometry in the form of horn-clause rules from math textbooks . |
to keep consistent , we initialize the embedding weight with pre-trained word embeddings . | we initialize the word embedding matrix with pre-trained glove embeddings . |
we use pre-trained vectors from glove for word-level embeddings . | for input representation , we used glove word embeddings . |
dagan and itai proposed an approach to wsd using monolingual corpora , a bilingual lexicon and a parser for the source language . | to overcome this problem , dagan and itai used a bilingual lexicon and statistical data from a monolingual corpus of the target language for disambiguation . |
the stanford parser we used produced parse trees with minor errors in some sentences . | we used the stanford parser to generate the grammatical structure of sentences . |
we show that the performance of such a classifier can be significantly improved by complementing it with a second-stage supervised classifier . | we show that a combination of both classifiers leads to significant improvements over using the unsupervised classifier alone . |
we pre-train the 200-dimensional word embeddings on each dataset in with skipgram . | for training , we pre-train the word vector and set the dimension to be 200 with skipgram . |
sentiment analysis is a multi-faceted problem . | sentiment analysis is the natural language processing ( nlp ) task dealing with the detection and classification of sentiments in texts . |
we employ widely used and standard machine translation tool moses to train the phrasebased smt system . | we use the open source moses phrase-based mt system to test the impact of the preprocessing technique on translation quality . |
tasks show that our method statistically significantly outperforms the baseline methods . | it also shows that our method significantly outperforms the baseline methods . |
we use a set of 318 english function words from the scikit-learn package . | we used the svd implementation provided in the scikit-learn toolkit . |
and finally , the baselines reported for resnik ¡¯ s test set were higher than those for the all-words task . | third and finally , the baselines reported for resnik¡¯s test set were higher than those for the all-words task . |
for language model , we used sri language modeling toolkit to train a 4-gram model with modified kneser-ney smoothing . | we use a fourgram language model with modified kneser-ney smoothing as implemented in the srilm toolkit . |
we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing . | for both languages , we used the srilm toolkit to train a 5-gram language model using all monolingual data provided . |
for nb and svm , we used their implementation available in scikit-learn . | we employed the machine learning tool of scikit-learn 3 , for training the classifier . |
mihalcea et al developed several corpus-based and knowledge-based word similarity measures and applied them to a paraphrase recognition task . | mihalcea et al proposed a method to measure the semantic similarity of words or short texts , considering both corpus-based and knowledge-based information . |
we show that using a post-processing morphology generation model can improve translation . | therefore , we propose a novel combination of post-processing morphology prediction with morpheme-based translation . |
we use 50 dimensional word embeddings , which are initialized by the 50 dimensional pre-trained word vectors 6 from glove , and updated in the training process . | we use the 300-dimensional pre-trained word2vec 3 word embeddings and compare the performance with that of glove 4 embeddings . |
we use the linear svm classifier from scikit-learn . | we implemented linear models with the scikit learn package . |
a pattern is a sequence of conditions that must hold true for a sequence of terms . | a pattern is a phrasal cons~ruc~ oi varyxng degrees of specificity . |
employment of tree kernel-based methods indicates the critical role of the structural information in zero anaphora resolution and the necessity of tree kernel methods in modeling such structural information . | this indicates the critical role of the structural information in zero anaphora resolution and the necessity of tree kernel-based methods in modeling such structural information . |
semantic parsing is the problem of deriving a structured meaning representation from a natural language utterance . | semantic parsing is the task of converting a sentence into a representation of its meaning , usually in a logical form grounded in the symbols of some fixed ontology or relational database ( cite-p-21-3-3 , cite-p-21-3-4 , cite-p-21-1-11 ) . |
we present a clean , human-annotated subset of 1975 question-document-answer triples . | we present triviaqa , a new dataset of 650k question-document-evidence triples . |