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importantly , word embeddings have been effectively used for several nlp tasks , such as named entity recognition , machine translation and part-of-speech tagging---word embeddings have shown promising results in nlp tasks , such as named entity recognition , sentiment analysis or parsing | 1 |
in this work , we address this problem of drift in tag distribution owing to adding training data from a supporting language---in this paper , we address the problem of divergence in tag distribution between primary and assisting languages | 1 |
for the mix one , we also train word embeddings of dimension 50 using glove---for input representation , we used glove word embeddings | 1 |
the idea of distant supervision has widely used in the task of relation extraction---in german , compounds and particle verbs , and show that our tree representation yields improvements in translation quality of 1 . 4 – 1 . 8 b leu in the wmt english – german translation task | 0 |
for our purpose we use word2vec embeddings trained on a google news dataset and find the pairwise cosine distances for all words---the evaluation method is the case insensitive ib-m bleu-4 | 0 |
this paper presented a dynamic layered model which takes full advantage of inner entity information to encourage outer entity recognition in an endto-end manner---text simplification ( ts ) is the task of modifying an original text into a simpler version of it | 0 |
lda is a generative probabilistic model where documents are viewed as mixtures over underlying topics , and each topic is a distribution over words---lda is one of the most common topic models which assumes each document is a mixture of various topics and each word is generated with multinomial distribution conditioned on a topic | 1 |
this model is also ‘ row-less ’ and does not directly model entities or entity pairs---that is ‘ row-less ’ having no explicit parameters for entity pairs and entities | 1 |
lin et al proposed a sub-tree extraction approach for argument identification---lin et al , 2014 ) proposed a tree subtraction algorithm to extract the arguments | 1 |
sentiment analysis is the natural language processing ( nlp ) task dealing with the detection and classification of sentiments in texts---sentiment analysis is the task of automatically identifying the valence or polarity of a piece of text | 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---we present the exemplar encoder-decoder network ( eed ) , a novel conversation model that learns to utilize similar examples from training data | 0 |
zoph et al use transfer learning to improve nmt from low-resource languages into english---we obtained both phrase structures and dependency relations for every sentence using the stanford parser | 0 |
results are reported using case-insensitive bleu with a single reference---translation scores are reported using caseinsensitive bleu with a single reference translation | 1 |
we train our svm classifiers using the liblinear package---for implementation , we used the liblinear package with all of its default parameters | 1 |
sentiment analysis is the task of identifying the polarity ( positive , negative or neutral ) of review---sentiment analysis is the process of identifying and extracting subjective information using natural language processing ( nlp ) | 1 |
our results will immediately benefit the large number of systems with apposition extraction components for coreference resolution and ie---the representations are typically either clusters of distributionally similar words , eg , brown et al , or vector representations | 0 |
in order to make full use of the dependency information , we assume that the target word math-w-10-1-0-72 is triggered by dependency edge of the corresponding source word math-w-10-1-0-83---the idea of searching a large corpus for specific lexicosyntactic phrases to indicate a semantic relation of interest was first described by hearst | 0 |
the danish dependency treebank comprises about 100k words of text selected from the danish parole corpus , with annotation of primary and secondary dependencies---ddt comprises 100k words of text selected from the danish parole corpus , with annotation of primary and secondary dependencies based on discontinuous grammar | 1 |
semantic parsing is the task of transducing natural language ( nl ) utterances into formal meaning representations ( mrs ) , commonly represented as tree structures---during the training process we built n-gram language models for use in decoding and rescoring using the kenlm language modelling toolkit | 0 |
scopes are the events modified by the trigger , syntactically realized as clauses , verb phrases , deverbal nouns or to-infinitives , according to al-sabbagh et al---we created 5-gram language models for every domain using srilm with improved kneserney smoothing on the target side of the training parallel corpora | 0 |
accordingly , automatization of this method is an important subject for study---automatization of this method remains an important issue to solve | 1 |
the hierarchical phrase-based model avoided this problem by introducing the glue rules 5 and 6 that combined hierarchical phrases sequentially---the hierarchical phrase-based model used hierarchical phrase pairs to strengthen the generalization ability of phrases and allow long distance reorderings | 1 |
in addition , this measure takes into account context dependent word importance information---context dependent word importance information improves performance | 1 |
chen et al and koo et al used large-scale unlabeled data to improve syntactic dependency parsing performance---chen et al presented an approach by using the information of adjacent words for indomain parsing | 1 |
the pol-yglot project mikolov et al developed an alternative solution for computing word embeddings , which significantly reduces the computational costs---mikolov et al have published word2vec , a toolkit that provides different possibilities to estimate word embeddings | 1 |
marcu and echihabi present the first approach focused on identifying implicit discourse relations---one of the first works that use statistical methods to detect implicit discourse relations is that of marcu and echihabi | 1 |
we define a conditional random field for this task---in this task , we used conditional random fields | 1 |
we perform our experiments on data sets from the english-to-czech translation task of wmt12 , wmt13 and wmt14---we perform our experiments on data sets from the english-to-czech translation task of wmt12 , wmt13 | 1 |
we use the well-known long short-term memory as our bi-rnn cell---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 | 0 |
we have also exploited , random and manually trained embeddings for initialization---in addition , we can use pre-trained neural word embeddings on large scale corpus for neural network initialization | 1 |
an assumption in almost all the previous models , however , posits that the learned representation ( e.g. , a distributed representation for a sentence ) , is fully compositional from the atomic components ( e.g. , representations for words ) , while non-compositionality is a basic phenomenon in human languages---in most previous models , however , posits that the learned representation ( e . g . , a distributed representation for a sentence ) is fully compositional from the atomic components ( e . g . , representations for words ) , while non-compositionality is a basic phenomenon in human languages | 1 |
srilm toolkit is used to build these language models---such as latent variable grammars and compositional vector grammars can be interpreted as special cases of lvegs | 0 |
hammarstr枚m and borin give an extensive overview of stateof-the-art unsupervised learning of morphology---hammarstr枚m and borin presents an interesting survey on unsupervised methods in morphology induction | 1 |
the tnt tagger and the treetagger are used for tagging and lemmatization---coreference resolution is the task of clustering a sequence of textual entity mentions into a set of maximal non-overlapping clusters , such that mentions in a cluster refer to the same discourse entity | 0 |
neural networks , working on top of conventional n-gram back-off language models , have been introduced in as a potential means to improve discrete language models---in this paper , we make a description of our submitted system to the semeval-2018 shared task | 0 |
we participated in the aspect term polarity subtask where the goal was to classify opinions related to a given aspect into positive , negative , neutral or conflict classes---where the goal was to classify opinions which are related to a given aspect into positive , negative , neutral or conflict classes | 1 |
we obtained word embeddings for our experiments by using the open source google word2vec 1---for creating the word embeddings , we used the tool word2vec 1 | 1 |
specifically , we employ the seq2seq model with attention implemented in opennmt---abstract meaning representations are a graph-based representation of the semantics of sentences | 0 |
work has also investigated whether scores on these dimensions correlate with language use---it has been shown that scores on these dimensions correlate with some aspects of language use | 1 |
we also want to make better use of the complex transition system to address the data sparsity issue for neural amr parsing---to address the data sparsity issue for neural amr parsing , we show that the transition state features are very helpful in constraining the possible output | 1 |
for this evaluation , we leverage rouge to address the relative quality of the generated summaries based on common ngram counts and longest common subsequence---similar to the evaluation for traditional summarization tasks , we use the rouge metrics to automatically evaluate the quality of produced summaries given the goldstandard reference news | 1 |
we initialize the embedding weights by the pre-trained word embeddings with 200 dimensional vectors---for the first two features , we adopt a set of pre-trained word embedding , known as global vectors for word representation | 1 |
for all settings , the results show that our method is able to detect annotation errors with high precision and high recall---as test cases , we showed that our model can detect errors with high precision and recall , and works especially well in an out-of-domain setting | 1 |
keyphrase extraction is a natural language processing task for collecting the main topics of a document into a list of phrases---keyphrase extraction is the problem of automatically extracting important phrases or concepts ( i.e. , the essence ) of a document | 1 |
we use glove word embeddings , which are 50-dimension word vectors trained with a crawled large corpus with 840 billion tokens---we use glove vectors with 200 dimensions as pre-trained word embeddings , which are tuned during training | 1 |
in this paper we presented the results of a corpus study of naturally occurring crs in task-oriented dialogue---in this paper , we describe a corpus study of crs in task-oriented dialogue | 1 |
dependency parsing is the task to assign dependency structures to a given sentence math-w-4-1-0-14---we use a conditional random field sequence model , which allows for globally optimal training and decoding | 0 |
in this paper , we proposed attr2vec , a novel embedding model that can jointly learn a distributed representation for words and contextual attributes---for example , ( cite-p-19-3-5 ) uses recursive neural networks to build representations of phrases and sentences | 0 |
in all submitted systems , we use the phrase-based moses decoder---information extraction ( ie ) is the task of extracting factual assertions from text | 0 |
event extraction is a particularly challenging type of information extraction ( ie )---event extraction is a particularly challenging information extraction task , which intends to identify and classify event triggers and arguments from raw text | 1 |
in order to objectively measure the quality of aspects , we use coherence score as a metric which has been shown to correlate well with human judgment---so the topic coherence metric is utilized to assess topic quality , which is consistent with human labeling | 1 |
the irstlm toolkit is used to build language models , which are scored using kenlm in the decoding process---the n-gram based language model is developed by employing the irstlm toolkit | 1 |
we used the sri language modeling toolkit for this purpose---the srilm toolkit was used to build this language model | 1 |
it uses semantic similarity between ontology terms and turn utterances to allow for parameter sharing between different slots across domains and within a single domain---that fully utilizes semantic similarity between dialogue utterances and the ontology terms , allowing the information to be shared across domains | 1 |
conditional random fields are probabilistic models for labelling sequential data---conditional random fields are undirected graphical models used for labeling sequential data | 1 |
the goal of our article is to propose novel methods for the analysis of the encoding of linguistic knowledge in rnns trained on language tasks---we present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure | 1 |
the language model was a 5-gram language model estimated on the target side of the parallel corpora by using the modified kneser-ney smoothing implemented in the srilm toolkit---we built a 5-gram language model on the english side of europarl and used the kneser-ney smoothing method and srilm as the language model toolkit | 0 |
we implement classification models using keras and scikit-learn---we used the scikit-learn library the svm model | 1 |
we train a 4-gram language model on the xinhua portion of the english gigaword corpus using the srilm toolkits with modified kneser-ney smoothing---we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus | 1 |
we use a bidirectional long short-term memory rnn to encode a sentence---we use long shortterm memory networks to build another semanticsbased sentence representation | 1 |
twitter is a popular microblogging service which provides real-time information on events happening across the world---twitter is a widely used microblogging environment which serves as a medium to share opinions on various events and products | 1 |
given the issued antecedent clause , the system generates the subsequent clause via sequential language modeling---given any specified antecedent clause , the system could generate a subsequent clause via sequential language modeling | 1 |
selectional preferences have also been a recent focus of researchers investigating the learning of paraphrases and inference rules---in addition , selectional preferences have been shown to be effective to improve the quality of inference and information extraction rules | 1 |
twitter is a widely used social networking service---twitter is the medium where people post real time messages to discuss on the different topics , and express their sentiments | 1 |
we use word2vec to train the word embeddings---we pre-train the word embeddings using word2vec | 1 |
work has been done on detecting relations within noun phrases , named entities , clauses and syntax-based comma resolution---work has been done on detecting relations between noun phrases , named entities , and clauses | 1 |
uedin has used the srilm toolkit to train the language model and relies on kenlm for language model scoring during decoding---this means in practice that the language model was trained using the srilm toolkit | 1 |
akkaya et al , martn-wanton et al deal with sentiment classification of sentences---akkaya et al , martn-wanton et al perform sentiment classification of individual sentences | 1 |
many words have multiple meanings , and the process of identifying the correct meaning , or sense of a word in context , is known as word sense disambiguation ( wsd )---word sense disambiguation ( wsd ) is formally defined as the task of computationally identifying senses of a word in a context | 1 |
by grouping opinion holders of different stances on diverse social and political issues , we can gain better understanding of the relationships among countries or among organizations---semantic applications typically extract information from intermediate structures derived from sentences , such as dependency | 0 |
to evaluate performance , we use the rouge-1 and rouge-2 f1 score , which correlate well with human rankings of summary quality---dependency parsing has been intensively studied in recent years | 0 |
the model parameters will then be estimated using the expectation-maximization algorithm---the parameters of the model are obtained by maximizing the likelihood of the observed data through expectationmaximisation algorithm | 1 |
sarcasm , commonly defined as ‘ an ironical taunt used to express contempt ’ , is a challenging nlp problem due to its highly figurative nature---in this paper , we present a latent variable model for one-shot dialogue response , and investigate what kinds of diversity | 0 |
sentence compression can potentially benefit many applications---sentence compression holds promise for many applications | 1 |
this scheme can be easily extended to work with a general ngram model---the model can be formalized as a synchronous context-free grammar | 1 |
all the feature weights and the weight for each probability factor are tuned on the development set with minimumerror-rate training---the feature weights are tuned to optimize bleu using the minimum error rate training algorithm | 1 |
subjectivity in natural language refers to aspects of language used to express opinions , feelings , evaluations , and speculations and it , thus , incorporates sentiment---subjectivity refers to the expression of emotions , sentiments , opinions , beliefs , speculations , evaluations , as well as other private states | 1 |
the translation quality is evaluated by case-insensitive bleu-4---the evaluation metric for the overall translation quality was case-insensitive bleu4 | 1 |
the training of the classifiers has been performed with scikit-learn---the models were implemented using scikit-learn module | 1 |
first , bansal et al showed that using word-embeddings can lead to significant improvement for dependency parsing---in bansal et al , better word embeddings for dependency parsing were obtained by using a corpus created to capture dependency context | 1 |
a 5-gram language model with kneser-ney smoothing was trained with srilm on monolingual english data---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit from stolcke | 1 |
ganter and strube proposed an approach for the automatic detection of sentences containing uncertainty based on wikipedia weasel tags and syntactic patterns---ganter and strube investigated wikipedia as a source of training data for the automatic hedge detection using word frequency measures and syntactic patterns | 1 |
our 5-gram language model is trained by the sri language modeling toolkit---we use sri language modeling toolkit to train a 5-gram language model on the english sentences of fbis corpus | 1 |
we employed the machine learning tool of scikit-learn 3 , for training the classifier---sentiment analysis is a nlp task that deals with extraction of opinion from a piece of text on a topic | 0 |
we use the word2vec framework in the gensim implementation to generate the embedding spaces---we initialize our word vectors with 300-dimensional word2vec word embeddings | 1 |
we used the encoderdecoder structure with the general attention mechanism---each candidate property ¡¯ s compatibility with the complementary simile component | 0 |
the second approach is a neural state-transition system over a set of explicit edit actions , including a designated copy action---our second approach is a neural state-transition system that explicitly learns the copy action | 1 |
we use the word2vec cbow model with a window size of 5 and a minimum frequency of 5 to generate 200-dimensional vectors---as embedding vectors , we used the publicly available representations obtained from the word2vec cbow model | 1 |
the model was built using the srilm toolkit with backoff and kneser-ney smoothing---metaphor is a figure of speech in which a word or phrase that ordinarily designates one thing is used to designate another , thus making an implicit comparison ( cite-p-19-1-11 , cite-p-19-1-12 , cite-p-19-3-15 ) | 0 |
in this paper , we trade off exact computation for enabling the use of more complex loss functions for coreference resolution ( cr )---after harvesting axioms from textbooks , we also present an approach to parse the axiom mentions to horn clause rules | 0 |
in this paper , we propose a novel unified model called siamese convolutional neural network for cqa---in this paper , we present a bootstrapping solution that exploits a large unannotated corpus for training | 0 |
hu and liu use wordnet synonyms and antonyms to bootstrap from words with known polarity to words with unknown polarity---specifically , hu and liu use wordnet synonyms and antonyms to predict the polarity of any given word with unknown polarity | 1 |
specifically , we use the portion converted automatically from part 3 of the penn arabic treebank to the catib format , which enriches the catib dependency trees with full patb morphological information---specifically , we use the portion converted from part 3 of the penn arabic treebank to the catib format , which enriches the catib dependency trees with full patb morphological information | 1 |
we exploit the svmlight-tk toolkit for kernel computation---we employ the ranking mode of the popular learning package svm light | 1 |
hulpus et al make use of the structured data in dbpedia 1 to label topics---the structured data in dbpedia is used to label topics | 1 |
recent work addresses this problem by scoring a particular dimension of essay quality such as coherence , technical errors , organization , and thesis clarity---recent work addresses this problem by scoring a particular dimension of essay quality such as coherence , technical errors , relevance to prompt , and organization | 1 |
marcu and wong propose a model to learn lexical correspondences at the phrase level---in this paper , we propose a bigram based supervised method for extractive document summarization | 0 |
twitter is a microblogging social network launched in 2006 with 310 million active users per month and where 340 million tweets are daily generated 1---sentiment analysis is a technique to classify documents based on the polarity of opinion expressed by the author of the document ( cite-p-16-1-13 ) | 0 |
experimental results show that whole-brain fmri data are significantly correlated with human judgement with respect to semantic similarity---dagan and itai proposed an approach to wsd using monolingual corpora , a bilingual lexicon and a parser for the source language | 0 |
we trained two 5-gram language models on the entire target side of the parallel data , with srilm---we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit | 1 |
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