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the word embeddings are pre-trained by skip-gram---we used word2vec to preinitialize the word embeddings | 1 |
a major component in phrase-based statistical machine translation is the table of conditional probabilities of phrase translation pairs---standard phrase-based machine translation uses relative frequencies of phrase pairs to estimate a translation model | 1 |
li and hoiem adopted this method to gradually add new capabilities to a multi-task system---li and hoiem adopted a method to gradually add new capabilities to a multi-task system while preserve the original capabilities | 1 |
the math-w-2-12-1-223h the pair of letters given so far---math-w-1-1-0-170 , the immediately preceding | 1 |
we present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences---we have presented multirc , a reading comprehension dataset in which questions require reasoning over multiple sentences to be answered | 1 |
relation extraction is the task of recognizing and extracting relations between entities or concepts in texts---relation extraction is the task of extracting semantic relationships between entities in text , e.g . to detect an employment relationship between the person larry page and the company google in the following text snippet : google ceo larry page holds a press announcement at its headquarters in new york on may 21 , 2012 | 1 |
we apply sri language modeling toolkit to train a 4-gram language model with kneser-ney smoothing---parsing is a computationally intensive task due to the combinatorial explosion seen in chart parsing algorithms that explore possible parse trees | 0 |
twitter is a social platform which contains rich textual content---twitter is a microblogging site where people express themselves and react to content in real-time | 1 |
coreference resolution is the next step on the way towards discourse understanding---coreference resolution is the task of determining which mentions in a text refer to the same entity | 1 |
in real settings , this can be useful when receiving a text message or when looking at anonymous posts in forums---in real settings , this can be useful when receiving a text message or when looking at anonymous posts | 1 |
we apply the 3-phase learning procedure proposed by where we first create word embeddings based on the skip-gram model---we use case-insensitive bleu as evaluation metric | 0 |
chen et al propose gated recursive neural networks , a variant of grconvs , to solve chinese word segmentation problem---chen et al proposed a gated recursive neural network to incorporate context information | 1 |
we have shown that ccg-gtrc as formulated above is weakly equivalent to ccg-std---we show that ccg-gtrc can actually be simulated by a ccg-std , proving the equivalence | 1 |
in our implementation , we use the binary svm light developed by joachims---in our implementation , we train the stance classifier using svm light | 1 |
xiong et al and seo et al employ variant coattention mechanism to match the question and passage mutually---seo et al and xiong et al applied different ways to match the question and the context with bidirectional attention | 1 |
finally we further enhance parsing by incorporating both structure and semantic constraints during decoding---inspired by these approaches , we also incorporate both structure and semantic constraints | 1 |
we used the moses machine translation decoder , using the default features and decoding settings---a trigram language model with modified kneser-ney discounting and interpolation was used as produced by the srilm toolkit | 0 |
coreference resolution is the task of clustering referring expressions in a text so that each resulting cluster represents an entity---the glove 100-dimensional pre-trained word embeddings are used for all experiments | 0 |
we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting---applying our story cloze classifier to this dataset yields 53 . 2 % classification accuracy | 0 |
semantic role labeling ( srl ) is the task of automatically annotating the predicate-argument structure in a sentence with semantic roles---semantic role labeling ( srl ) is the task of labeling predicate-argument structure in sentences with shallow semantic information | 1 |
furthermore , we employ unsupervised topic models to detect the topics of the queries as well as to enrich the target taxonomy---to get the the sub-fields of the community , we use latent dirichlet allocation to find topics and label them by hand | 1 |
we use maxent modeling as the learning component---we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing | 0 |
hochreiter and schmidhuber , 1997 ) and bidirectional lstm have been effective in modeling sequential information---lstms were introduced by hochreiter and schmidhuber in order to mitigate the vanishing gradient problem | 1 |
we use moses , a statistical machine translation system that allows training of translation models---neural models , with various neural architectures , have recently achieved great success | 0 |
wikipedia is a web based , freely available multilingual encyclopedia , constructed in a collaborative effort by thousands of contributors---wikipedia is the largest collection of encyclopedic data ever written in the history of humanity | 1 |
there has also been much interest in applying sequence-to-sequence models for abstractive sentence compression---more recently , there has been much interest in applying neural network models to natural language generation tasks , including sentence compression | 1 |
derivatives are computed efficiently via backpropagation through structure---sememes are defined as minimum semantic units of word meanings , and there exists a limited close set of sememes to compose the semantic | 0 |
faruqui et al apply post-processing steps to existing word embeddings in order to bring them more in accordance with semantic lexicons such as ppdb and framenet---faruqui et al use synonym relations extracted from wordnet and other resources to construct an undirected graph | 1 |
ctransr is an extension of transr by clustering diverse head-tail entity pairs into groups and learning distinct relation vectors for each group---in different relations , ctransr clusters diverse head-tail entity pairs into groups and sets a relation vector for each group | 1 |
in this paper , we propose an attention based lstm network for cross-language sentiment classification---in this study , we propose an attention-based bilingual representation learning model which learns the distributed semantics of the documents | 1 |
information extraction ( ie ) is the process of identifying events or actions of interest and their participating entities from a text---information extraction ( ie ) is the task of extracting factual assertions from text | 1 |
the phrase translation strategy significantly outperformed the sentence translation strategy---we use conditional random field sequence labeling as described in | 0 |
srl is the process by which predicates and their arguments are identified and their roles are defined in a sentence---srl is the task of identifying arguments for a certain predicate and labelling them | 1 |
word embeddings have proven to be effective models of semantic representation of words in various nlp tasks---convolutional neural networks are useful in many nlp tasks , such as language modeling , semantic role labeling and semantic parsing | 1 |
according to lakoff and johnson , metaphors are cognitive mappings of concepts from a source to a target domain---lakoff and johnson argue that metaphor is a method for transferring knowledge from a concrete domain to an abstract domain | 1 |
inspired by this observation , we propose a simple sentence selector to select the minimal set of sentences to feed into the qa model---we represent input words using pre-trained glove wikipedia 6b word embeddings | 0 |
we use dynet 5 to implement our neural models , and automatic batch technique in dynet to perform mini-batch gradient descent training---we use the automatic batch technique in dynet to perform mini-batch gradient descent training | 1 |
alexina is a framework that represents lexical information in a complete , efficient and readable way standard---alexina is a framework compatible with the lmf 3 standard , whose goal is to represent lexical information in a complete , efficient and readeable way | 1 |
we use sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus---we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus | 1 |
mikolov et al , 2013a mikolov et al , 2013b have demonstrated state-of-the-art performance using a neural embedding model with an efficient objective function called word2vec---more recently , mikolov et al , 2013a mikolov et al , 2013c introduced the skip-gram model which utilizes a simplified neural network architecture for learning vector representations of words from unstructured text data | 1 |
socher et al applied recursive autoencoders to address sentencelevel sentiment classification problems---socher et al propose to use recursive neural networks to learn syntactic-aware compositionality upon words | 1 |
we used scikit-lean toolkit , and we developed a framework to define functional classification models---in particular , we use the liblinear svm 1va classifier | 0 |
the word embeddings were obtained using word2vec 2 tool---the embeddings have been trained with word2vec on twitter data | 1 |
for example , vanderwende uses semantic relations extracted from ldoce to interpret nominal compounds---collobert and weston used convolutional neural networks in a multitask setting , where their model is trained jointly for multiple nlp tasks with shared weights | 0 |
the deep learning model uses a pooled bidirectional gated recurrent unit architecture---a gated recurrent unit neural network is employed to construct the context embedding and response embedding | 1 |
to train a crf model , we use the wapiti sequence labelling toolkit---we choose the crf learning toolkit wapiti 1 to train models | 1 |
in this paper , we propose efficient and less resource-intensive strategies for parsing of code-mixed data---in this paper , we have evaluated different strategies for parsing code-mixed data | 1 |
the hierarchical phrase-based model has been widely adopted in statistical machine translation---the entropy pruning criterion could be applied to hierarchical machine translation systems | 1 |
in this paper , we introduce automatic ‘ drunk-texting prediction ’ as a computational task---in this paper , we introduce automatic drunk-texting prediction as the task of predicting a tweet | 1 |
coreference resolution is the task of determining which mentions in a text refer to the same entity---for evaluation we use mteval-v13a from the moses toolkit and tercom 3 to score our systems on the bleu respectively ter measures | 0 |
coreference resolution is a well known clustering task in natural language processing---since coreference resolution is a pervasive discourse phenomenon causing performance impediments in current ie systems , we considered a corpus of aligned english and romanian texts to identify coreferring expressions | 1 |
relation extraction ( re ) is the process of generating structured relation knowledge from unstructured natural language texts---we tune model weights using minimum error rate training on the wmt 2008 test data | 0 |
here , we proposed an ensemble feature selection method which takes advantage of many different types of feature selection criteria in feature selection---following the idea , this paper proposes a new ensemble feature selection method which is capable of extracting good features from different feature classes | 1 |
distributional semantic models produce vector representations which capture latent meanings hidden in association of words in documents---distributional semantic models induce large-scale vector-based lexical semantic representations from statistical patterns of word usage | 1 |
we use the logistic regression classifier as implemented in the skll package , which is based on scikitlearn , with f1 optimization---we applied liblinear via its scikitlearn python interface to train the logistic regression model with l2 regularization | 1 |
we used the phrase-based model moses for the experiments with all the standard settings , including a lexicalized reordering model , and a 5-gram language model---we used the state-ofthe-art phrase-based model for statistical machine translation with several non-standard settings , eg , data selection and phrase table combination | 1 |
we use bleu scores to measure translation accuracy---the data we use comes from the penn arabic treebank | 0 |
in addition , we use yago database for distant supervision---with the help of the yago knowledge , we borrow the distant supervision technique | 1 |
meanwhile , we adopt glove pre-trained word embeddings 5 to initialize the representation of input tokens---we use pre-trained 50 dimensional glove vectors 4 for word embeddings initialization | 1 |
lee and seneff , 2008 ) proposed an approach based on pattern matching on trees combined with word n-gram counts for correcting agreement misuse and some types of verb form errors---besides , lee and seneff propose a method to correct verb form errors through combining the features of parse trees and n-gram counts | 1 |
coreference resolution is the task of identifying all mentions which refer to the same entity in a document---reliability of self-labeled data is an important issue when the data are regarded as ground-truth | 0 |
negation is well-understood in grammars , the valid ways to form a negation are well-documented---negation is well-understood in grammars and the valid ways to express negation are documented | 1 |
the brown algorithm is a hierarchical clustering algorithm which clusters words to maximize the mutual information of bigrams---in a knowledge graph , we train the rnn model for generating natural language questions from a sequence of keywords | 0 |
following , we use a neural network with two hidden layers to learn distributed word feature vectors from large-scale training data---the 2017 clinical tempeval challenge is the most recent community challenge that addresses temporal information extraction from clinical notes | 0 |
for language model , we use a trigram language model trained with the srilm toolkit on the english side of the training corpus---we use a fourgram language model with modified kneser-ney smoothing as implemented in the srilm toolkit | 1 |
we trained two 5-gram language models on the entire target side of the parallel data , with srilm---we train a kn-smoothed 5-gram language model on the target side of the parallel training data with srilm | 1 |
we were able to show that performance improves with increased depth , using up to 29 convolutional layers---we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 0 |
as is now standard for feature-based grammars , we mainly use log-linear models for parse selection---we measure the translation quality using a single reference bleu | 0 |
mei et al propose an encoder-aligner-decoder architecture to generate weather forecasts---mei et al proposed an encoder-aligner-decoder framework for generating weather broadcast | 1 |
conditional random fields is a popular and efficient ml technique for supervised sequence labeling---conditional random fields are a convenient formalism for sequence labeling tasks common in nlp | 1 |
here we call a sequence of words which have lexicai cohesion relation with each other a lezical chain like---here we call a sequence of words which have lexical cohesion relation with each other a lezical chain like | 1 |
choi et al , 2005 ) used the named entities to identify the opinion holders with the help of machine learning and pattern-based techniques---during the last decade , statistical machine translation systems have evolved from the original word-based approach into phrase-based translation systems | 0 |
most phrase-based smt systems use the translation probability and the lexical weighting as the parameters of scoring functions for translated phrases---our proposed methods are useful for raising the accuracy of a multi-class document categorization | 0 |
however , barzilay and mckeown did similar work to corpus-based identification of general paraphrases from multiple english translations of the same source text---with the improved the grammar and ontology , we will use the knowledge learned to extend our model to words not in lexeed , using definition | 0 |
our experimental evaluation shows that our approach significantly outperforms strong baselines on the ap metric---our experimental evaluation shows that our new framework significantly outperforms strong baselines | 1 |
we used yamcha 1 , which is a general purpose svm-based chunker---we use a support vector machine -based chunker yamcha for the chunking process | 1 |
for building our ap e b2 system , we set a maximum phrase length of 7 for the translation model , and a 5-gram language model was trained using kenlm---for building our statistical ape system , we used maximum phrase length of 7 and a 5-gram language model trained using kenlm | 1 |
a 5-gram language model was created with the sri language modeling toolkit and trained using the gigaword corpus and english sentences from the parallel data---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 | 1 |
the word embeddings and attribute embeddings are trained on the twitter dataset using glove---semantic embeddings are glove trained on twitter data 1 , word2vec , mi- | 1 |
additionally , coreference resolution is a pervasive problem in nlp and many nlp applications could benefit from an effective coreference resolver that can be easily configured and customized---coreference resolution is the process of linking together multiple expressions of a given entity | 1 |
our model uses non-negative matrix factorization in order to find latent dimensions---our model uses non-negative matrix factorization -nmf in order to find latent dimensions | 1 |
information extraction ( ie ) is a fundamental technology for nlp---coreference resolution is a key problem in natural language understanding that still escapes reliable solutions | 0 |
within this subpart of our ensemble model , we used a svm model from the scikit-learn library---for nb and svm , we used their implementation available in scikit-learn | 1 |
16 computational linguistics , volume 14 , number 1 , winter 19---2 computational linguistics , volume 14 , number 1 , winter 1988 | 1 |
conditional random fields are used to calculate the conditional probability of values on designated output nodes given values on other designated input nodes---conditional random fields are undirected graphical models to calculate the conditional probability of values on designated output nodes given values on designated input nodes | 1 |
here we use stanford corenlp toolkit to deal with the co-reference problem---we use the sentiment pipeline of stanford corenlp to obtain this feature | 1 |
in this paper , we study the language of memes by jointly learning the image , the description , and the popular votes---in this paper , we statistically study the correlations among popular memes and their wordings , and generate meme | 1 |
we experimented with the phrase-based smt model as implemented in moses---we applied the ems in moses to build up the phrase-based translation system | 1 |
we used srilm to build a 4-gram language model with interpolated kneser-ney discounting---we trained a 4-gram language model on this data with kneser-ney discounting using srilm | 0 |
there is a wealth of prior work on multilingual pos tagging---in this paper , we thoroughly review the work on multilingual pos tagging | 1 |
a 4-gram language model generated by sri language modeling toolkit is used in the cube-pruning process---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 1 |
it is well-known that readers are less likely to fixate their gaze on closed class syntactic categories such as prepositions and pronouns---and it is well-known that readers are more likely to fixate on words from open syntactic categories ( verbs , nouns , adjectives ) than on closed category items | 1 |
coreference resolution is a task aimed at identifying phrases ( mentions ) referring to the same entity---coreference resolution is the problem of partitioning a sequence of noun phrases ( or mentions ) , as they occur in a natural language text , into a set of referential entities | 1 |
training is done through stochastic gradient descent over shuffled mini-batches with the adagrad update rule---in this paper , we systematically explore a large space of features for relation extraction | 0 |
the experiments were carried out using the chinese-english datasets provided within the iwslt 2006 evaluation campaign , extracted from the basic travel expression corpus---the word embeddings are initialized with 100-dimensions vectors pre-trained by the cbow model | 0 |
in this paper , we present a comprehensive analysis of the relationship between personal traits and brand preferences---in this study , we focus on investigating the feasibility of using automatically inferred personal traits | 1 |
designing these transformation rules is a major undertaking which requires multiple correction cycles and a deep understanding of the underlying grammar formalisms---design of these rules is a major linguistic and computational undertaking , which requires multiple iterations over the data | 1 |
ritter et al proposed an smt based method , which treats response generation as a machine translation task---ritter et al first introduced the mt technique into response generation | 1 |
twitter is a communication platform which combines sms , instant messages and social networks---twitter is the medium where people post real time messages to discuss on the different topics , and express their sentiments | 1 |
our dataset consists of 12,045 records of adult deaths from the million death study , which is a program to collect and code vas from india---although coreference resolution is a subproblem of natural language understanding , coreference resolution evaluation metrics have predominately been discussed in terms of abstract entities and hypothetical system errors | 0 |
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