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we use the glove vectors of 300 dimension to represent the input words---we use glove vectors with 200 dimensions as pre-trained word embeddings , which are tuned during training | 1 |
we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit from stolcke | 1 |
we use bleu scores to measure translation accuracy---we measure the translation quality using a single reference bleu | 1 |
the state-of-the-art baseline is a standard phrase-based smt system tuned with mert---the various smt systems are evaluated using the bleu score | 0 |
our trigram word language model was trained on the target side of the training corpus using the srilm toolkit with modified kneser-ney smoothing---we created 5-gram language models for every domain using srilm with improved kneserney smoothing on the target side of the training parallel corpora | 1 |
lexical simplification is a subtask of text simplification ( cite-p-16-3-3 ) concerned with replacing words or short phrases by simpler variants in a context aware fashion ( generally synonyms ) , which can be understood by a wider range of readers---lexical simplification is the task to find and substitute a complex word or phrase in a sentence with its simpler synonymous expression | 1 |
word sense disambiguation ( wsd ) is a key enabling-technology that automatically chooses the intended sense of a word in context---we implement classification models using keras and scikit-learn | 0 |
wu et al used the bachrach et al corpus to investigate the correlation between changes in embedding depth and reading times and found a positive effect on latency---wu et al found that increasing the embedding depth led to longer reading times in a self-paced reading experiment | 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---we trained a trigram language model on the chinese side , with the srilm toolkit , using the modified kneser-ney smoothing option | 1 |
twitter is a popular microblogging service which provides real-time information on events happening across the world---twitter is the medium where people post real time messages to discuss on the different topics , and express their sentiments | 1 |
this paper sets out to study critical tokenization , a distinctive type of tokenizationfollowing the principle of maximum tokenization---in this paper , we have also discussed some important implications of the notion of critical tokenization in the area of character string tokenization | 1 |
the translation results are evaluated by caseinsensitive bleu-4 metric---results are reported using case-insensitive bleu with a single reference | 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 identifying the polarity ( positive , negative or neutral ) of review | 1 |
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 )---word sense disambiguation ( wsd ) is formally defined as the task of computationally identifying senses of a word in a context | 1 |
coreference resolution is a multi-faceted task : humans resolve references by exploiting contextual and grammatical clues , as well as semantic information and world knowledge , so capturing each of these will be necessary for an automatic system to fully solve the problem---grammar induction is the task of inducing high-level rules for application of grammars in spoken dialogue systems | 0 |
we use the word2vec vectors with 300 dimensions , pre-trained on 100 billion words of google news---one of the first challenges in sentiment analysis is the vast lexical diversity of subjective language | 0 |
blitzer et al investigate domain adaptation for sentiment classifiers , focusing on online reviews for different types of products---blitzer et al and tan et al implemented domain adaptation strategies for sentiment analysis | 1 |
we have introduced a method for converting dependency structures to logical forms using the lambda calculus---we present a robust method for mapping dependency trees to logical forms that represent underlying predicate-argument structures | 1 |
recently , v茅ronis has proposed hyperlex , an application of graph models to wsd based on the small-world properties of cooccurrence graphs---v茅ronis proposed a graph based model named hyperlex based on the small-world properties of co-occurrence graphs | 1 |
we evaluated our model in both supervised and semi-supervised scenarios over multiple languages , and show it can outperform other supervised and semi-supervised methods---on eight different languages , show that the ncrf-ae model can outperform competitive systems in both supervised and semi-supervised scenarios | 1 |
we apply this novel learning algorithm to pos tagging---and our algorithm is based on perceptron learning | 1 |
information extraction ( ie ) is the task of identifying information in texts and converting it into a predefined format---information extraction ( ie ) is a main nlp aspects for analyzing scientific papers , which includes named entity recognition ( ner ) and relation extraction ( re ) | 1 |
related work bharati et al has described a constraint based hindi parser by applying the paninian framework---bharati et al has described a constraint based hindi parser by applying the paninian framework | 1 |
we train a linear support vector machine classifier using the efficient liblinear package---we use the svm implementation available in the li-blinear package | 1 |
we use svm-light-tk 5 , which enables the use of structural kernels---to calculate the constituent-tree kernels st and sst we used the svm-light-tk toolkit | 1 |
research on automatic semantic structure extraction has been widely studied since the pioneering work of gildea and jurafsky---ever since the pioneering article of gildea and jurafsky , there has been an increasing interest in automatic semantic role labeling | 1 |
abstract meaning representation is a semantic formalism where the meaning of a sentence is encoded as a rooted , directed graph---models that rely on 1-best parses are prone to learn noisy translation rules in training phase and produce degenerate translations in decoding phase | 0 |
we use the word2vec skip-gram model to train our word embeddings---we use the pre-trained word2vec embeddings provided by mikolov et al as model input | 0 |
this paper presents a minimal but surprisingly effective span-based neural model for constituency parsing---in this work , we present a minimal neural model for constituency parsing | 1 |
the log-lineal combination weights were optimized using mert---the model weights were trained using the minimum error rate training algorithm | 1 |
this paper presents an algorithm that detects and corrects speech repairs based on finding the repair pattern---in this paper , we present an algorithm that detects and corrects modification and abridged speech repairs | 1 |
in this paper , we presented a supervised classification model for keyphrase extraction from scientific research papers that are embedded in citation networks---in all cases , we used the implementations from the scikitlearn machine learning library | 0 |
morris and hirst connect highly related words in the discourse to create chains , which indicate cohesion of ideas in text---morris and hirst and kozima find topic boundaries in the texts by using lexical cohesion | 1 |
we use the mert algorithm for tuning and bleu as our evaluation metric---adaption of kneser-ney smoothing to graphs may be useful for research in subgraph mining | 0 |
choi et al and mao and lebanon are representative of the structured sentiment analysis approach which takes advantage of conditional random fields to determine sentiment flow---choi et al and mao and lebanonare representative of the structured sentiment analysis approach which takes advantage of conditional random fields to determine sentiment flow | 1 |
as a measure of the working memory capacity , the japanese version of a reading span test was conducted---as a measure of the working memory capacity , the japanese version of the reading span test was conducted | 1 |
ttea is an acronym for text engineering architecture---agate is an acronym for general architecture for text engineering | 1 |
collobert and weston , in their seminal paper on deep architectures for nlp , propose a multilayer neural network for learning word embeddings---collobert and weston used convolutional neural networks in a multitask setting , where their model is trained jointly for multiple nlp tasks with shared weights | 1 |
brockett et al showed that phrase-based statistical mt can help to correct mistakes made on mass nouns---brockett et al use smt to correct countability errors for a set of 14 mass nouns that pose problems to chinese esl learners | 1 |
charniak , 1996 ) and observed that treebank grammars are very large and grow with the size of the treebank---in charniak and krotov et al , it was observed that treebank grammars are very large and grow with the size of the treebank | 1 |
as the grammar is based on a monostratal theory of grammar , annotation by manual disambiguation determines syntactic and semantic structure at the same time---we used svm multiclass from svm-light toolkit as the classifier | 0 |
we use skip-gram representation for the training of word2vec tool---we use word2vec tool for learning distributed word embeddings | 1 |
in our model , we introduce a sentinel to control the tradeoff between background knowledge and information from the text---from the currently processed text , our model employs an attention mechanism with a sentinel to adaptively decide whether to attend to background knowledge | 1 |
erk and pad贸 proposed a corpus-based method that does not rely on type vectors---as the query contains some keywords which could help in sharpening the focus of the summary | 0 |
the srilm toolkit is used to train 5-gram language model---the language models were trained using srilm toolkit | 1 |
retrieval effectiveness is found to be strongly influenced by the translation quality of the queries---we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit | 0 |
the mert implementation uses the line search of cer et al to directly minimize corpus-level error---rating of the target word could be a useful clue for determining whether the sense is literal or metaphorical | 0 |
we adapted the moses phrase-based decoder to translate word lattices---we used a phrase-based smt model as implemented in the moses toolkit | 1 |
transition-based dependency parsing was originally introduced by yamada and matsumoto and nivre---yamada and matsumoto proposed a deterministic classifierbased parser | 1 |
a pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy , homonymy , or phonological similarity to another word , for an intended humorous or rhetorical effect---a pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy , homonymy , or phonological similarity to another word , for an intended humorous or rhetorical effect ( cite-p-15-3-1 ) | 1 |
shen et al proposed a string-to-dependency target language model to capture long distance word orders---shen et al and mi and liu develop a generative dependency language model for string-to-dependency and tree-to-tree models | 1 |
the feature weights are tuned with minimum error-rate training to optimise the character error rate of the output---the nnlm weights are optimized as the other feature weights using minimum error rate training | 1 |
coreference resolution is the task of identifying all mentions which refer to the same entity in a document---coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world | 1 |
we used moses as the phrase-based machine translation system---in addition , we can use pre-trained neural word embeddings on large scale corpus for neural network initialization | 0 |
sentiment classification is the task of identifying the sentiment polarity of a given text---we introduced a novel , more difficult task combining hypernym detection and directionality , and showed that our methods outperform a frequency baseline | 0 |
our baseline system is phrase-based moses with feature weights trained using mert---we develop translation models using the phrase-based moses smt system | 1 |
the scikit-learn library was used for the svm , which utilized a polynomial kernel with degree of 4---here , they were an inspiration for our sentence relatedness function | 0 |
greedy string tiling allows to deal with reordered text parts as it determines a set of shared contiguous substrings , whereby each substring is a match of maximal length---with nonlinear models , we show that word embeddings with substring features is an effective representation | 0 |
keyphrase extraction is the task of extracting a selection of phrases from a text document to concisely summarize its contents---keyphrase extraction is a fundamental technique in natural language processing | 1 |
we trained a standard 5-gram language model with modified kneser-ney smoothing using the kenlm toolkit on 4 billion running words---we estimated unfiltered 5-gram language models using lmplz and loaded them with kenlm | 1 |
sentiment analysis is the task of identifying positive and negative opinions , sentiments , emotions and attitudes expressed in text---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 ) | 1 |
hence , we use gibbs sampling by casella and george to estimate the underlying distributions---we use gibbs sampling for inference to both the parametric and nonparametric model | 1 |
we initialize our word vectors with 300-dimensional word2vec word embeddings---the nodes are concepts ( or synsets as they are called in the wordnet ) | 0 |
secondly , we present an unsupervised way to construct a set of relation topics at multiple scales---second , we construct a set of non-redundant relation topics defined at multiple scales | 1 |
read used emoticons from a training set downloaded from usenet newsgroups as annotations---read used emoticons from a training set that was downloaded from usenet newsgroups as annotations | 1 |
we use mteval from the moses toolkit and tercom to evaluate our systems on the bleu and ter measures---we use mteval from the moses toolkit an tercom to evaluate our systems on the bleu and ter measures | 1 |
multi-task learning using a related auxiliary task can lead to stronger generalization and better regularized models---seventy-five teams ( about 200 team members ) participated in the shared task | 0 |
semantic parsing is the task of converting natural language utterances into their complete formal meaning representations which are executable for some application---semantic parsing is the mapping of text to a meaning representation | 1 |
as embedding vectors , we used the publicly available representations obtained from the word2vec cbow model---for data preparation and processing we use scikit-learn | 0 |
previous work has focused on automatically learning and integrating translations of very specific mwe categories , such as , for instance , idiomatic chinese four character expressions or domain specific mwes---some works have however focused on automatically learning translations of very specific mwes categories , such as , for instance , idiomatic four character expressions in chinese or domain specific mwes | 1 |
thus , while these analyses are reliable for the examples they focus on , they can not be generalized to other examples---in graph-based learning approaches one constructs a graph whose vertices are labeled and unlabeled examples , and whose weighted edges encode the degree to which the examples they link have the same label | 0 |
for generating the translations from english into german , we used the statistical translation toolkit moses---for phrase-based smt translation , we used the moses decoder and its support training scripts | 1 |
we initialize the embedding weights by the pre-trained word embeddings with 200 dimensional vectors---we initialize our word vectors with 300-dimensional word2vec word embeddings | 1 |
latent dirichlet allocation is a generative probabilistic topic model where documents are represented as random mixtures over latent topics , characterized by a distribution over words---we trained the statistical phrase-based systems using the moses toolkit with mert tuning | 0 |
probabilistic context-free grammars underlie most high-performance parsers in one way or another---at present , most high-performance parsers are based on probabilistic context-free grammars in one way or another | 1 |
for language model , we use a trigram language model trained with the srilm toolkit on the english side of the training corpus---we build an open-vocabulary language model with kneser-ney smoothing using the srilm toolkit | 1 |
we use a word2vec model pretrained on 100 billion words of google news---on all datasets and models , we use 300-dimensional word vectors pre-trained on google news | 1 |
we will ( try to ) show how both deictic and anaphoric references can be resolved using a single model---we present a single model that accounts for referent resolution of deictic and anaphoric expressions | 1 |
bengio et al use distributed representations for words to fight the curse of dimensionality when training a neural probabilistic language model---for instance , bengio et al present a neural probabilistic language model that uses the n-gram model to learn word embeddings | 1 |
we show in this paper that following this intuition leads to suboptimal results---in this paper , we show in extensive experiments that following this intuition leads to suboptimal results | 1 |
for both systems , we used the berkeley aligner with default settings to align the parallel data---we aligned both bitexts with the berkeley aligner configured with standard settings | 1 |
we use pre-trained word2vec word vectors and vector representations by tilk et al to obtain word-level similarity information---we use the popular word2vec 1 tool proposed by mikolov et al to extract the vector representations of words | 1 |
the mert implementation uses the line search of cer et al to directly minimize corpus-level error---cer et al explored regularization of mert to improve generalization on test sets | 1 |
hence , in recent years , there is a research trend towards statistical dialogue management---in recent years , there is a growing interest in sharing personal opinions on the web , such as product reviews , economic analysis , political polls , etc | 1 |
a number of corpus-based studies on translation have shown that it is possible to automatically predict whether a text is an original or a translation---gong et al extend this by further introducing two additional caches | 0 |
sentiment analysis is the task in natural language processing ( nlp ) that deals with classifying opinions according to the polarity of the sentiment they express---sentiment analysis is a nlp task that deals with extraction of opinion from a piece of text on a topic | 1 |
generation is a pervasive relation between action descriptions in naturally occurring data---generation is a relation over lead to my goal of finding out how she is feeling | 1 |
in they demonstrated that using label propagation with twitter follower graph improves the polarity classification---in authors demonstrated that using label propagation with twitter follower graph improves the polarity classification | 1 |
we use the wn similarity jcn score since this gave reasonable results for and it is efficient at run time given precompilation of frequency information---we use the wn similarity jcn score since this gave reasonable results for mccarthy et al and it is efficient at run time given precompilation of frequency information | 1 |
in this paper we show that a multi-view ensemble approach that leverages simple representations of texts may achieve good results in the task of message polarity classification---in this paper , we presented a multi-view ensemble approach to message polarity classification that participated in the semeval-2017 task | 1 |
a rumor may be defined as a statement whose truth value is unverified or deliberately false---rumor is commonly defined as information that emerge and spread among people whose truth value is unverified or intentionally false | 1 |
part-of-speech tagging is the act of assigning each word in a sentence a tag that describes how that word is used in the sentence---part-of-speech tagging is the assignment of syntactic categories ( tags ) to words that occur in the processed text | 1 |
we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model---our trigram word language model was trained on the target side of the training corpus using the srilm toolkit with modified kneser-ney smoothing | 1 |
previous work showed that word clusters derived from an unlabelled dataset can improve the performance of many nlp applications---previous work has shown that unlabeled text can be used to induce unsupervised word clusters which can improve the performance of many supervised nlp tasks | 1 |
we also use a 4-gram language model trained using srilm with kneser-ney smoothing---we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting | 1 |
the feature weights 位 m are tuned with minimum error rate training---the log-lineal combination weights were optimized using mert | 1 |
in this paper , we describe a fast algorithm for sentence alignment that uses lexical information---in pronoun resolution is guided by extending the centering theory from the grammatical level to the semantic level | 0 |
we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus---we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing | 1 |
the decoder needs this information to proactively perform memory addressing---to enable the decoder to actively interact with a memory | 1 |
in this paper , we take the standard lstm with peephole connections ( cite-p-24-1-13 ) as a baseline---in this paper , we take the standard lstm with peephole connections ( cite-p-24-1-13 ) | 1 |
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