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our empirical results demonstrate that incorporating eye gaze with recognition hypotheses consistently outperforms the results obtained from processing recognition hypotheses alone---given the mobility of the user , our results have shown that incorporating eye gaze with recognition hypotheses consistently outperform the results obtained from processing recognition hypotheses alone | 1 |
distributed word representations induced through deep neural networks have been shown to be useful in several natural language processing applications---convolutional neural networks are useful in many nlp tasks , such as language modeling , semantic role labeling and semantic parsing | 1 |
srilm toolkit has been used to develop the language models using target language sentences from the training and tuning sets of parallel corpora---the probabilistic language model is constructed on google web 1t 5-gram corpus by using the srilm toolkit | 1 |
multi-task joint learning can transfer knowledge between tasks by sharing task-invariant layers---semantic role labeling ( srl ) is the task of labeling predicate-argument structure in sentences with shallow semantic information | 0 |
barzilay and mckeown proposed an idea called sentence fusion that integrates information in overlapping sentences to produce a nonoverlapping summary sentence---barzilay and mckeown propose a text-to-text generation technique for synthesizing common information across documents using sentence fusion | 1 |
mitchell and lapata investigate several vector composition operations for representing short sentences---mitchell and lapata presented a framework for representing the meaning of phrases and sentences in vector space | 1 |
the parameters are optimized with adagrad under a cosine proximity objective function---we train the parameters of the stages separately using adagrad with the perceptron loss function | 1 |
for parameter training we use conditional random fields as described in---for this supervised structure learning task , we choose the approach conditional random fields | 1 |
hagiwara et al perform synonym identification based on both distributional and contextual features---hagiwara et al identified synonyms using a supervised approach relying on distributional and syntactic features | 1 |
this paper proposes a novel , probabilistic approach to reordering which combines the merits of syntax and phrase-based smt---to smt , this paper proposes a novel , probabilistic approach to reordering which combines the merits of syntax and phrase-based smt | 1 |
for the tree-based system , we applied a 4-gram language model with kneserney smoothing using srilm toolkit trained on the whole monolingual corpus---we first establish the importance of the sentence id feature space for cross-lingual word embedding algorithms | 0 |
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 the medium where people post real time messages to discuss on the different topics , and express their sentiments | 1 |
in addition , mixed feature sets also show potential for scaling well when dealing with larger number of verbs and verb classes---feature sets perform comparatively well on the tasks that involve more classes ( e . g . 14-way ) , exhibiting the tendency to scale well with larger number of verb classes and verbs | 1 |
table 1 summarizes test set performance in bleu , nist and ter---our experiments indicate that mem significantly outperforms prior work | 0 |
we use stanford log-linear partof-speech tagger to produce pos tags for the english side---to generate dependency links , we use the stanford pos tagger 18 and the malt parser | 1 |
semantic role labeling ( srl ) is the task of automatically labeling predicates and arguments in a sentence with shallow semantic labels---semantic role labeling ( srl ) is the task of identifying semantic arguments of predicates in text | 1 |
we used the sri language modeling toolkit to train lms on our training data for each ilr level---we used kenlm with srilm to train a 5-gram language model based on all available target language training data | 1 |
translation results are evaluated using the word-based bleu score---the translation quality is evaluated by caseinsensitive bleu-4 metric | 1 |
multi-label text categorization is a type of text categorization , where each document is assigned to one or more categories---we have presented a novel discriminative language model using pseudo-negative examples | 0 |
unfortunately , we have seen that this kind of theory can not explain opaque indexicals---i will describe this approach and show why it fails to explain the opacity of indexicals | 1 |
this paper describes a novel stacked subword model---in this work , we propose a novel stacked subword model for this task | 1 |
in this section , we evaluate the robustness of the automatic image captioning metrics---for this reason , we used glove vectors to extract the vector representation of words | 0 |
liu and gildea used semantic features for a tree-to-string syntax based smt system---similar works relied on named entities , language models allan et al , 2003 , contexts , etc | 0 |
deep neural networks have shown great promises at capturing salient features for these complex tasks---recently , convolutional neural networks have yielded best performance on many text classification tasks | 1 |
goldberg and zhu also used in-domain labeled data to approximate sentiment similarity for semi-supervised sentiment classification---goldberg and zhu presented a graphbased semi-supervised learning algorithm for the sentiment analysis task of rating inference | 1 |
macaon is a tool suite for standard nlp tasks developed for french---macaon is a suite of tools developped to process ambiguous input and extend inference of input modules within a global scope | 1 |
for example , okura et al proposed to learn representations of news using denoising autoencoder and learn representations of users from their browsed news using gru network---for example , okura et al proposed to learn representations of news from news bodies using denoising autoencoder , and learn representations of users from the representations of their browsed news using a gru network | 1 |
the systems were automatically evaluated using bleu on held-out evaluation sets---acquisition is significantly improved when temporal information is considered | 0 |
we apply the rules to each sentence with its dependency tree structure acquired from the stanford parser---we obtained both phrase structures and dependency relations for every sentence using the stanford parser | 1 |
document summarization can be treated as a special kind of translation process : translating from a bunch of related source documents to a short target summary---summarization can be seen as a special kind of machine translation : translating the original documents into a brief summary | 1 |
the language model was a kneser-ney interpolated trigram model generated using the srilm toolkit---language models were built using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
we use the sequential minimal optimization algorithm from weka and the feature set mentioned above for all experiments---for the classification , we use the smo algorithm from weka , setting 10-fold cross validation as a testing option | 1 |
this model first embeds the words using 300 dimensional word embeddings created using the glove method---the word vectors of vocabulary words are trained from a large corpus using the glove toolkit | 1 |
stance detection has been defined as automatically detecting whether the author of a piece of text is in favor of the given target or against it---sentiment analysis is the task of identifying positive and negative opinions , sentiments , emotions and attitudes expressed in text | 0 |
srilm can be used to compute a language model from ngram counts---a 4-grams language model is trained by the srilm toolkit | 1 |
the data was processed using the standard moses pipeline , specifically , punctuation normalization , tokenization and truecasing---the statistical phrase-based systems were trained using the moses toolkit with mert tuning | 1 |
after standard preprocessing of the data , we train a 3-gram language model using kenlm---turney and littman proposed to compute pair-wised mutual information between a target word and a set of seed positive and negative words to infer the so of the target word | 0 |
text summarization is the task of automatically condensing a piece of text to a shorter version while maintaining the important points---generating a condensed version of a passage while preserving its meaning is known as text summarization | 1 |
empirically , s-lstm can give effective sentence encoding after 3 ¨c 6 recurrent steps---however , s-lstm models hierarchical encoding of sentence structure as a recurrent state | 1 |
cooperative , corrective and self-directing discourse knowledge are designed and integrated to mimic such type of users---cooperative , corrective and self-directing discourse knowledge are designed to mimic such type user | 1 |
interestingly convolutional neural networks , widely used for image processing , have recently emerged as a strong class of models for nlp tasks---convolutional neural networks have recently achieved remarkably strong performance also on the practically important task of sentence classification | 1 |
figure 1 : example of ¡°temporal graph¡± : around the pope¡¯s death---figure 2 : example of ¡° temporal graph ¡± : madrid | 1 |
as corpus resource we relied on decow14ax , a german web corpus containing 12 billion tokens---we use the german web corpus decow14ax containing 12 billion tokens , with the 10,000 most common nouns as vector dimensions | 1 |
contrary to the vast interest in open ie , its task formulation has been largely overlooked---in spite of this broad attention , the open ie task definition has been lacking | 1 |
a potential solution to this problem is to use weakly-supervised ml instead---weakly-supervised learning could be employed to improve the practical | 1 |
as regards syntactic chunking , jess-cm significantly outperformed aso-semi for the same 15m-word unlabeled data size obtained from the wall street journal in 1991 as described in ( cite-p-18-1-0 )---as regards syntactic chunking , jess-cm significantly outperformed aso-semi for the same 15m-word unlabeled data size obtained from the wall street journal in 1991 | 1 |
attention-based neural machine translation systems are typically implemented with a recurrent neural network based encoder-decoder framework---inspired by the success of neural machine translation , recent studies use the encoder-decoder model with the attention mechanism | 1 |
the classic work on this task was by bagga and baldwin , who adapted the vector space model---bagga and baldwin used the vector space model together with summarization techniques to tackle the crossdocument coreference problem | 1 |
a handful of papers have studied system combination for summarization---long short term memory units are proposed in hochreiter and schmidhuber to overcome this problem | 0 |
we trained a 4-gram language model on this data with kneser-ney discounting using srilm---we set the feature weights by optimizing the bleu score directly using minimum error rate training on the development set | 0 |
a complication is that sets of grs are useful for purposes---for japanese-to-english task , we use a chunkbased japanese dependency tree | 0 |
in this paper , we apply quantitative approaches to understand the dynamics of the counseling interactions and their relation to counselor empathy---in this paper , we explore several aspects pertaining to counseling interaction dynamics and their relation to counselor empathy | 1 |
in order to efficiently train parameters , we apply a reparameterization technique ( cite-p-22-3-6 , cite-p-22-1-10 ) on the variational lower bound---we present a new model for acquiring comprehensive multiword lexicons from large corpora | 0 |
we posit that there is a latent mapping of the question-answer meaning representation graph onto the text meaning representation graph that explains the answer---we posit that there is a hidden structure that explains the correctness of an answer given the question and instructional materials and present a unified max-margin framework that learns to find these hidden structures ( given a corpus of question-answer pairs and instructional materials ) , and uses what it learns to answer novel elementary science questions | 1 |
for the language model we use the corpus of 60,000 simple english wikipedia articles 3 and build a 3-gram language model with kneser-ney smoothing trained with srilm---we use srilm train a 5-gram language model on the xinhua portion of the english gigaword corpus 5th edition with modified kneser-ney discounting | 1 |
brown clustering is a greedy , hierarchical , agglomerative hard clustering algorithm to partition a vocabulary into a set of clusters with minimal loss in mutual information---the weights of the different feature functions were tuned by means of minimum error-rate training executed on the europarl development corpus | 0 |
the bleu , rouge and ter scores by comparing the abstracts before and after human editing are presented in table 5---automatic evaluation results in terms of bleu scores are provided in table 2 | 1 |
a pun is the exploitation of the various meanings of a word or words with phonetic similarity but different meanings---a pun is a word used in a context to evoke two or more distinct senses for humorous effect | 1 |
named entity recognition is a traditinal task of the natural language processing domain---named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance | 1 |
multi-task learning using a related auxiliary task can lead to stronger generalization and better regularized models---multi-task learning via neural networks have been used to model relationships among the correlated tasks | 1 |
the ud scheme is built on the google universal part-of-speech tagset , the interset interlingua of morphosyntactic features , and stanford dependencies---the annotation scheme is based on an evolution of stanford dependencies and google universal part-of-speech tags | 1 |
srilm toolkit is used to build these language models---the srilm toolkit was used to build the trigram mkn smoothed language model | 1 |
a 4-grams language model is trained by the srilm toolkit---we implement an in-domain language model using the sri language modeling toolkit | 1 |
we train a 4-gram language model on the xinhua portion of english gigaword corpus by srilm toolkit---the target-side language models were estimated using the srilm toolkit | 0 |
our translation model is implemented as an n-gram model of operations using the srilm toolkit with kneser-ney smoothing---topic modeling is a useful mechanism for discovering and characterizing various semantic concepts embedded in a collection of documents | 0 |
the datasets are released and publicly available via http : //www.teds---datasets and the newly created ones are released and available at http : / / www . teds | 1 |
we use the cnn model with pretrained word embedding for the convolutional layer---we train distributional similarity models with word2vec for the source and target side separately | 1 |
the language model is a trigram-based backoff language model with kneser-ney smoothing , computed using srilm and trained on the same training data as the translation model---the target language model was a trigram language model with modified kneser-ney smoothing trained on the english side of the bitext using the srilm tookit | 1 |
in schwenk and gauvain and later in schwenk research was performed on training large scale neural network language models on millions of words resulting in a decrease of the word error rate for continuous speech recognition---it was followed by schwenk who applied neural network for language modeling in large scale vocabulary speech recognition and obtained a noticeable improvement in word error rate | 1 |
we used the 300-dimensional glove word embeddings learned from 840 billion tokens in the web crawl data , as general word embeddings---we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing | 0 |
for relation classification , socher et al proposed a recursive matrix-vector model based on constituency parse trees---the english side of the parallel corpus is trained into a language model using srilm | 0 |
sentiment classification is a hot research topic in natural language processing field , and has many applications in both academic and industrial areas ( cite-p-17-1-16 , cite-p-17-1-12 , cite-p-17-3-4 , cite-p-17-3-3 )---sentiment classification is a well-studied and active research area ( cite-p-20-1-11 ) | 1 |
coreference resolution is the process of linking together multiple expressions of a given entity---coreference resolution is the task of partitioning the set of mentions of discourse referents in a text into classes ( or ‘ chains ’ ) corresponding to those referents ( cite-p-12-3-14 ) | 1 |
while reranking has benefited many tagging and parsing tasks including semantic role labeling , it has not yet been applied to semantic parsing---we performed the annotation on the brat annotation tool | 0 |
bleu and rouge are the standard similarity metrics used in machine translation and text summarisation---in this paper , we present an approach that extracts subtrees from dependency trees in auto-parsed data | 0 |
word sense disambiguation ( wsd ) is a task to identify the intended sense of a word based on its context---we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing | 0 |
huang et al used svm to automatically extract and rank title-reply pairs from online discussion forums for chatbot knowledge---huang et al used an svm classifier to extract pairs as chat knowledge from online discussion forums to support the construction of a chatbot for a certain domain | 1 |
we use the word2vec skip-gram model to train our word embeddings---we use word embeddings 3 as a cheap low-maintenance alternative for knowledge base construction | 1 |
we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model---we train a 5-gram language model with the xinhua portion of english gigaword corpus and the english side of the training set using the srilm toolkit | 1 |
we make use of moses toolkit for this paradigm---we used a phrase-based smt model as implemented in the moses toolkit | 1 |
we used the srilm toolkit to build unpruned 5-gram models using interpolated modified kneser-ney smoothing---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 1 |
sentence ranking is a crucial part of generating text summaries---the system is based on the transformer implementation in opennmt-py | 0 |
we use glove vectors with 200 dimensions as pre-trained word embeddings , which are tuned during training---for this paper , we directly utilize the pre-trained fasttext word embeddings model which is trained on wikipedia data | 0 |
we used the srilm toolkit and kneser-ney discounting for estimating 5-grams lms---we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
underlying the semantic roles approach is a lexicalist assumption , that is , each verb¡¯s lexical entry completely encodes ( more formally , projects ) its syntactic and semantic structures---under a lexicalist approach to semantics , a verb completely encodes its syntactic and semantic structures , along with the relevant syntax-to-semantics | 1 |
language models used modified kneserney smoothing estimated using kenlm---the language model was trained using kenlm toolkit with modified kneser-ney smoothing | 1 |
sentiment analysis is a research area in the field of natural language processing---coherence is the property of a good human-authored text that makes it easier to read and understand than a randomly-ordered collection of sentences | 0 |
we analyze the deficiency of traditional outer attention-based rnn models qualitatively and quantitatively---we embed math-w-5-4-2-68 as the term-level model | 0 |
our manual evaluation of the reordering accuracy indicated that the reordering approach is helpful at improving the translation quality despite relatively frequent reordering errors---in this paper , we propose a generative model that jointly identifies user-proposed refinements in instruction reviews | 0 |
wang et al , proposed an attention based lstm which introduced the aspect clues by concatenating the aspect embeddings and the word representations---we used 14 datasets , most of which are non-projective , from the conll 2006 and 2008 shared tasks | 0 |
the smt system is implemented using moses and the nmt system is built using the fairseq toolkit---the statistical phrase-based systems were trained using the moses toolkit with mert tuning | 1 |
lu et al modeled senses of a real ambiguous word by picking out the most similar monosemous morpheme from a chinese hierarchical lexicon---senses of a real ambiguous word have been modeled by picking out the most similar monosemous morpheme from a chinese hierarchical lexicon | 1 |
rindflesch et al use hand-coded rule-based systems to extract the factual assertions from biomedical text---rindflesch et al use hand-coded rule based systems to extract the factual assertions from biomedical text | 1 |
liu et al proposed a new structure named weighted alignment matrix that make a better use of noisy alignments---chinese – english and german – english show our model to be significantly better than the phrase-based model | 0 |
cui et al proposed a system utilizing fuzzy relation matching guided by statistical models---cui et al developed an information theoretic measure based on dependency trees | 1 |
we trained the three classifiers using the svm implementation in scikit-learn , and tuned hyper-parameters c and 纬 using 10-fold cross-validation with the train split---we used implementations from scikitlearn , and the parameters of both classifiers were tuned on the development set using grid search | 1 |
such semantic-oriented dependency structures have been shown very helpful for nlp applications e.g . question answering ( cite-p-26-1-29 )---dependency analysis provides a useful approximation to the underlying meaning representations , and has been shown very helpful for nlp applications e . g . question answering ( cite-p-26-1-29 ) | 1 |
faruqui et al introduced retrofitting of word vectors based on external ontologies , such as wordnet or ppdb---faruqui et al proposed to retrofit pre-trained embeddings to semantic lexicons | 1 |
this was the best performing method on average in the 2016 semeval task 6 shared task---stance detection was one of the tasks in the semeval-2016 shared task competition | 1 |
language models were built using the srilm toolkit 16---trigram language models are implemented using the srilm toolkit | 1 |
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