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spanish is the third-most used language on the internet , after english and chinese , with a total of 7.7 % of internet users ( more than 277 million of users ) and a huge users growth of more than 1,400 %---however , spanish is the third language most used on the internet , with a total of 7.7 % ( more than 277 million of users ) and a huge internet growth of more than 1,400 % | 1 |
the probabilistic language model is constructed on google web 1t 5-gram corpus by using the srilm toolkit---a 4-gram language model is trained on the monolingual data by srilm toolkit | 1 |
in recent years , statistical approaches on atr ( automatic term recognition ) have achieved good results---in recent years , statistical approaches on atr ( automatic term recognition ) | 1 |
however , the reliability of the self-labeled data is an important issue---word sense disambiguation ( wsd ) is the task of determining the correct meaning or sense of a word in context | 0 |
zhu et al in contrast present an approach based on syntax-based smt---zhu et al suggest a probabilistic , syntaxbased approach to text simplification | 1 |
coreference resolution is a complex problem , and successful systems must tackle a variety of non-trivial subproblems that are central to the coreference task — e.g. , mention/markable detection , anaphor identification — and that require substantial implementation efforts---coreference resolution is the task of partitioning a set of entity mentions in a text , where each partition corresponds to some entity in an underlying discourse model | 1 |
twitter is a famous social media platform capable of spreading breaking news , thus most of rumour related research uses twitter feed as a basis for research---twitter is a microblogging site where people express themselves and react to content in real-time | 1 |
huang et al further extended this context clustering method and incorporated global context to learn multi-prototype representation vectors---huang et al presented an rnn model that uses document-level context information to construct more accurate word representations | 1 |
we use two standard evaluation metrics bleu and ter , for comparing translation quality of various systems---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 ) | 0 |
table 3 shows the results in bleu , translation edit rate , and position-independent word-error rate , obtained with moses and our hierarchical phrase-based smt , respectively---table 2 presents the translation performance in terms of various metrics such as bleu , meteor and translation edit rate | 1 |
discrimination clearly plays a major role in the disambiguation task , but it less clear whether it is still relevant when disambiguation is not an issue , that is , in the case of referential overspecification---discrimination-which normally plays a major role in the disambiguation task-is also a major influence in referential overspecification , even though disambiguation is in principle not relevant | 1 |
wikipedia is a large , multilingual , highly structured , multi-domain encyclopedia , providing an increasingly large wealth of knowledge---we evaluate the translation quality using the case-insensitive bleu-4 metric | 0 |
also , we initialized all of the word embeddings using the 300 dimensional pre-trained vectors from glove---additionally , a back-off 2-gram model with goodturing discounting and no lexical classes was built from the same training data , using the srilm toolkit | 0 |
this work is an attempt to automatically obtain numerical attributes of physical objects---work is an attempt to automatically obtain knowledge on numerical attributes | 1 |
table 1 summarizes test set performance in bleu , nist and ter---automatic evaluation results are shown in table 1 , using bleu-4 | 1 |
collobert et al employ a cnn-crf structure , which obtains competitive results to statistical models---collobert et al adjust the feature embeddings according to the specific task in a deep neural network architecture | 1 |
in the reranking stage , we use linearly combined model of these models---in the reranking stage is performed using linear interpolation of these models | 1 |
huang et al presented an rnn model that uses document-level context information to construct more accurate word representations---huang et al train their vectors with a neural network and additionally take global context into account | 1 |
in all submitted systems , we use the phrase-based moses decoder---for all submissions , we used the phrase-based variant of the moses decoder | 1 |
the result of the process is a list of potential support verbs for the nominalized form of a given predicate---the result of this process is a narrative representation graph ( nrg ) | 1 |
we apply sri language modeling toolkit to train a 4-gram language model with kneser-ney smoothing---our 5-gram language model is trained by the sri language modeling toolkit | 1 |
for math-w-5-5-0-3 , let math-w-5-5-0-14 where math-w-5-5-0-22 , math-w-5-5-0-30 the initial learning rate---for math-w-5-5-0-3 , let math-w-5-5-0-14 where math-w-5-5-0-22 , math-w-5-5-0-30 | 1 |
word sense disambiguation ( wsd ) is a key enabling-technology---word sense disambiguation ( wsd ) is formally defined as the task of computationally identifying senses of a word in a context | 1 |
hildebrand et al selected comparable sentences from parallel corpora using information retrieval techniques---hildebrand et al used an information retrieval method for translation model adaptation | 1 |
one of the central challenges in sentiment-based text categorization is that not every portion of a document is equally informative for inferring the overall sentiment of the document---one of the central challenges in sentiment-based text categorization is that not every portion of a given document is equally informative for inferring its overall sentiment | 1 |
in its third year , the task provided 19 training and 20 testing datasets for 8 languages and 7 domains , as well as a common evaluation procedure---in its third year , the semeval absa task provided 19 training and 20 testing datasets , from 7 domains and 8 languages | 1 |
coreference resolution is a task aimed at identifying phrases ( mentions ) referring to the same entity---we used the implementation of the scikit-learn 2 module | 0 |
the incremental parsing process of our parser is based on the shift-reduce parsers of sagae and lavie and wang et al , with slight modifications---our parser is based on the shift-reduce parsing process from sagae and lavie and wang et al , and therefore it can be classified as a transition-based parser , | 1 |
in conclusion , this paper exploits the relevance between term candidates as an additional feature for term extraction approach---in this study , the relevance between term candidates are iteratively calculated by graphs | 1 |
we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus---for this language model , we built a trigram language model with kneser-ney smoothing using srilm from the same automatically segmented corpus | 1 |
venugopal et al propose a method to watermark the output of machine translation systems to aid this distinction---sentiment classification is a useful technique for analyzing subjective information in a large number of texts , and many studies have been conducted ( cite-p-15-3-1 ) | 0 |
previous such work operates at the word level---wall street journal dataset , is available at the authors ’ website at http : / / goo . gl / roqeh | 0 |
we find that embedding methods alleviate sparsity concerns of pattern-based approaches and substantially improve coverage---embeddings of pattern-based models substantially improves performance by remedying the sparsity issue | 1 |
we investigate an effective way to use sentiment lexicon features---first , we introduce sentiment lexicon features , which effectively improve classification | 1 |
in the mt domain , xiong et al attempt to improve lexical coherence with a topic-based model---the log-linear parameter weights are tuned with mert on the development set | 0 |
we feed our features to a multinomial naive bayes classifier in scikit-learn---for training our system classifier , we have used scikit-learn | 1 |
the system described in this paper is the grandchild of the first transition-based neural network dependency parser ( cite-p-22-3-1 ) , which was the university of geneva ’ s entry in the conll 2007 multilingual dependency parsing shared task ( cite-p-22-1-7 )---the system presented in this paper is a modification of the one published in cite-p-14-1-1 | 1 |
this paper proposes a novel intention level user simulation technique---this paper proposes a novel user intention simulation method which is a data-driven approach | 1 |
experiments show that our approach improves performance , especially in oov-recall---data show that our approach improves performance , especially in oov-recall | 1 |
socher et al introduce a semi-supervised approach that uses recursive autoencoders to learn the hierarchical structure and sentiment distribution of a sentence---socher et al defined a recurrent neural network model , which , in essence , learns those polarity shifters relying on sentence-level sentiment labels | 1 |
experiments on 12 cross-specialty ner tasks show that la-dtl provides consistent performance improvement over strong baselines---to our knowledge , this work represents the first attempt to aid in the process of discovering de operators , a task whose importance | 0 |
translation quality is evaluated by case-insensitive bleu-4 metric---the evaluation metric for the overall translation quality was case-insensitive bleu4 | 1 |
wang et al utilized attention-based lstm , which takes into account aspect information during attention---in the work of wang et al , a variant of attention-based lstm was proposed | 1 |
the alignment template model enhanced phrasal generalizations by using words classes rather than the words themselves---the alignment template approach uses word classes rather than lexical items to model phrase translation | 1 |
in addition , a 5-gram lm with kneser-ney smoothing and interpolation was built using the srilm toolkit---the target-side language models were estimated using the srilm toolkit | 0 |
word sense disambiguation ( wsd ) is a task to identify the intended sense of a word based on its context---word sense disambiguation ( wsd ) is the task of automatically determining the correct sense for a target word given the context in which it occurs | 1 |
relation extraction is the task of detecting and classifying relationships between two entities from text---relation extraction is the task of finding relational facts in unstructured text and putting them into a structured ( tabularized ) knowledge base | 1 |
in this demonstration , we present t he p rojector , an interactive gui designed to assist researchers in such analysis : it allows users to execute and visually inspect annotation projection in a range of different settings---in this demonstration , we present t he p rojector , an interactive gui designed to assist researchers in such analysis : it allows users to execute and visually inspect annotation projection | 1 |
for example , riaz and girju and do et al introduced unsupervised metrics to learn causal dependencies between events---the word embeddings are initialized with 100-dimensions vectors pre-trained by the cbow model | 0 |
semantic role labeling ( srl ) is a major nlp task , providing a shallow sentence-level semantic analysis---the topic co-reference resolution resembles another well-known problem in nlp -the noun phrase co-reference resolution that considers machine learning frameworks | 0 |
when training a classifier for one label , predictions-as-features methods can model dependencies between former labels and the current label , but they can ’ t model dependencies between the current label and the latter labels---for one label , the predictions-as-features methods can model dependencies between former labels and the current label , but they can ’ t model dependencies between the current label and the latter labels | 1 |
for the sick and msrvid experiments , we used 300-dimension glove word embeddings---with regard to inputs , we use 50-d glove word embeddings pretrianed on wikipedia and gigaword and 5-d postion embedding | 0 |
solving automatic algebra word problems can be viewed as a semantic parsing task---automatically solving algebra word problems has raised considerable interest | 1 |
linear svm classifiers are a highly robust supervised classification method that has proven to be very effective for text classification---all smt models were developed using the moses phrase-based mt toolkit and the experiment management system | 0 |
gru is a special kind of rnn , which is widely used for learning long-term dependencies---lstm and gru are a special kind of rnn , capable of learning long-term dependencies | 1 |
we use a fourgram language model with modified kneser-ney smoothing as implemented in the srilm toolkit---to calculate language model features , we train traditional n-gram language models with ngram lengths of four and five using the srilm toolkit | 1 |
the language model is a 5-gram with interpolation and kneser-ney smoothing---we use srilm to build 5-gram language models with modified kneser-ney smoothing | 1 |
in this paper , we propose an entity-focused , hybrid generation approach to automatically produce descriptions of previously unseen companies , and show that it outperforms a strong summarization baseline---in this paper , we propose an entity-focused system using a combination of targeted ( knowledge base driven ) and data-driven generation to create company descriptions | 1 |
one direction is to explore better vector comparison methods that will utilize the improved feature weighting , as shown in geffet and dagan---a demonstration of such potential appears in geffet and dagan , which presents a novel feature inclusion scheme for vector comparison | 1 |
the em algorithm is an incremental approach to clustering , which fits parameters of gaussian density distributions to the data---the forward-backward algorithm , a version of the em algorithm , is specifically designed for unsupervised parameter estimation of hmm models | 1 |
a relatively more recent approach for slu is based on conditional random fields---in this paper , we took a focused form of humorous tercets in hindi-dur se dekha , and performed an analysis of its structure and humour | 0 |
for language model , we use a trigram language model trained with the srilm toolkit on the english side of the training corpus---further , we apply a 4-gram language model trained with the srilm toolkit on the target side of the training corpus | 1 |
we used minimum error rate training to optimize the feature weights---we performed mert based tuning using the mira algorithm | 1 |
yih et al use an array of lexical semantic similarity resources , from which they derive features for a binary classifier---yih et al constructed semantic features from wordnet and paired semantically related words based on these features and relations | 1 |
by contrast , aspects of semantic interpretation , such as reference and quantifier scope resolution , are often realised by non-monotonic operations involving loss of information and destructive manipulation of semantic representations---scoping and reference resolution , are often realised computationally by non-monotonic operations involving loss of information and destructive manipulation of semantic representations | 1 |
one of the basic and most widely used models is latent dirichlet allocation---a widely used topic modeling method is the latent dirichlet allocation model , which is proposed by blei | 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---coreference resolution is the task of grouping all the mentions of entities 1 in a document into equivalence classes so that all the mentions in a given class refer to the same discourse entity | 0 |
the smoothness assumption can actually be imposed to a wide variety of kg embedding models---linear embedding are used to model the smoothness assumption | 1 |
our system participated in semeval-2013 task 2 : sentiment analysis in twitter ( cite-p-12-3-1 )---system that participated in semeval-2013 task 2 : sentiment analysis in twitter | 1 |
we perform minimum error rate training to tune various feature weights---we use our reordering model for n-best re-ranking and optimize bleu using minimum error rate training | 1 |
semantic role labeling ( srl ) is the task of identifying the semantic arguments of a predicate and labeling them with their semantic roles---semantic role labeling ( srl ) consists of finding the arguments of a predicate and labeling them with semantic roles ( cite-p-9-1-5 , cite-p-9-3-0 ) | 1 |
simple zero-inflated models can account for practically relevant variation , and can be easier to work with than overdispersed models---using statistics from both standard and learner corpora , it generates plausible distractors | 0 |
the phrase-based translation systems rely on language model and lexicalized reordering model to capture lexical dependencies that span phrase boundaries---we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing | 0 |
we use 300d glove vectors trained on 840b tokens as the word embedding input to the lstm---mihalcea et al translated english subjectivity words and phrases into the target language to build a lexicon-based classifier | 0 |
the smt systems are tuned on the dev development set with minimum error rate training using bleu accuracy measure as the optimization criterion---the feature weights of the translation system are tuned with the standard minimum-error-ratetraining to maximize the systems bleu score on the development set | 1 |
in this work , we employ the toolkit word2vec to pre-train the word embedding for the source and target languages---we use the pre-trained word2vec embeddings provided by mikolov et al as model input | 1 |
the word embeddings are initialized by pre-trained glove embeddings 2---we initialized our word embeddings with glove 100-dimensional embeddings 7 | 1 |
the language models are 4-grams with modified kneser-ney smoothing which have been trained with the srilm toolkit---we use srilm to build 5-gram language models with modified kneser-ney smoothing | 1 |
in this paper , we do model adaptation using a neural network framework---hochreiter and schmidhuber developed long short-term memory to overcome the long term dependency problem | 0 |
word embedding approaches like word2vec or glove are powerful tools for the semantic analysis of natural language---we trained word vectors with the two architectures included in the word2vec software | 0 |
named entity recognition ( ner ) is a key technique for ie and other natural language processing tasks---named entity recognition ( ner ) is a well-known problem in nlp which feeds into many other related tasks such as information retrieval ( ir ) and machine translation ( mt ) and more recently social network discovery and opinion mining | 1 |
our proposed approach first train a continuous bag-of-words model from a large collection of raw text to generate word embeddings---following , we develop a continuous bag-of-words model that can effectively model the surrounding contextual information | 1 |
for subword granularity , we use the bpe method to merge 30k and 32k steps---for word splitting in sub-word units , we use the byte pair encoding tools from the subword-nmt toolkit | 1 |
we employ srilm toolkit to linearly interpolate the target side of the training corpus with the wmt english corpus , optimizing towards the mt tuning set---we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus | 1 |
we implement an in-domain language model using the sri language modeling toolkit---our 5-gram language model is trained by the sri language modeling toolkit | 1 |
the annotation scheme is derived from the universal stanford dependencies , the google universal part-of-speech tags and the interset interlingua for morphological tagsets---the annotation scheme leans on the universal stanford dependencies complemented with the google universal pos tagset and the interset interlingua for morphological tagsets | 1 |
word sense disambiguation ( wsd ) is the nlp task that consists in selecting the correct sense of a polysemous word in a given context---li et al suggested a grapheme-based joint source-channel model within the direct orthographic mapping framework | 0 |
in our experiments , we show performance gains for several language pairs , 17 % for top-10 precision for math-w-2-7-1-65---in our experiments , we show performance gains for several language pairs , 17 % for top-10 precision | 1 |
we use a random forest classifier , as implemented in scikit-learn---zarrie脽 and kuhn argue that multiword expressions can be reliably detected in parallel corpora by using dependency-parsed , word-aligned sentences | 0 |
bengio et al presented a neural network language model where word embeddings are simultaneously learned along with a language model---for example , bengio et al introduced a model that learns word vector representations as part of a simple neural network architecture for language modeling | 1 |
wikipedia is a web based , freely available multilingual encyclopedia , constructed in a collaborative effort by thousands of contributors---for word embeddings , we used popular pre-trained word vectors from glove | 0 |
here , we present an effective , expandable , and tractable new approach to comprehensive multiword lexicon acquisition---we present a new model for acquiring comprehensive multiword lexicons from large corpora | 1 |
choi et al examine opinion holder extraction using crfs with several manually defined linguistic features and automatically learnt surface patterns---choi et al explore oh extraction using crfs with several manually defined linguistic features and automatically learnt surface patterns | 1 |
random indexing is an approach which incrementally builds word vectors in a dimensionally-reduced space---we present kb-u nify , a novel approach for integrating the output of different open information | 0 |
in this paper , we presented details of mayonlp¡¯s participation in the scienceie share task at semeval 2017---this paper presents a large-scale system for the recognition and semantic disambiguation of named entities | 0 |
this model is also ¡®row-less¡¯ and does not directly model entities or entity pairs---first , arabic is a morphologically rich language ( cite-p-19-3-7 ) | 0 |
minimum error rate training is applied to tune the cn weights---the feature weights 位 m are tuned with minimum error rate training | 1 |
peters et al propose a deep neural model that generates contextual word embeddings which are able to model both language and semantics of word use---peters et al show that their language model elmo can implicitly disambiguate word meaning with their contexts | 1 |
semantic parsing is a domain-dependent process by nature , as its output is defined over a set of domain symbols---semantic parsing is the task of mapping natural language utterances to machine interpretable meaning representations | 1 |
moreover , al-sabbagh and girju described an approach of mining the web to build a da-to-msa lexicon---al-sabbagh and girju described an approach of mining the web to build a da-to-msa lexicon | 1 |
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