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we used the sri language modeling toolkit with kneser-kney smoothing---sentiment analysis is the task of identifying positive and negative opinions , sentiments , emotions and attitudes expressed in text | 0 |
we initialize the embedding weights by the pre-trained word embeddings with 200 dimensional vectors---hasegawa et al tried to extract multiple relations by choosing entity types | 0 |
marton et al also explore which morphological features could be useful in dependency parsing of arabic---in marton et al , we investigated morphological features for dependency parsing of modern standard arabic | 1 |
we use the vector offset method to compute the missing word in these relations---then , we use word embedding generated by skip-gram with negative sampling to convert words into word vectors | 1 |
for amr parsing , our model achieves competitive results of 62.1 smatch , the current best score reported without significant use of external semantic resources---within this subpart of our ensemble model , we used a svm model from the scikit-learn library | 0 |
in this paper , we propose a non-linear modeling of translation hypotheses based on neural networks---in this paper , we discuss a non-linear framework for modeling translation | 1 |
we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus---we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting | 1 |
we use the wordsim353 dataset , divided into similarity and relatedness categories---we use wordsim-353 , which contains 353 english word pairs with human similarity ratings | 1 |
additionally , we tested our system on drugnerar corpus , which similarly focuses on drug interactions---furthermore , we also evaluated the system on a similarly drug-focused corpus annotated for anaphora | 1 |
in future work , we would like to extend the clustering algorithm to not use a fixed number of target clusters but to depend on the number of natural clusters the data falls into---in future work , we would like to extend the clustering algorithm to not use a fixed number of target clusters but to depend on the number of natural clusters | 1 |
jing proposes a novel algorithm for sentence reduction that takes into account different sources of information to decide whether or not to remove a particular component from a sentence to be included in a summary---jing also studied a method to remove extraneous phrases from sentences by using multiple source of knowledge to decide which phrase can be removed | 1 |
relation extraction is the task of finding relationships between two entities from text---relation extraction is a challenging task in natural language processing | 1 |
in this paper , we propose structural embedding of syntactic trees ( sest ) that encode syntactic information structured by constituency tree and dependency tree into neural attention models for the question answering task---in this paper , we propose structural embedding of syntactic trees ( sest ) , an algorithm framework to utilize structured information and encode them into vector representations | 1 |
according to cite-p-16-1-0 and our observations , adjectival verbs are verbs that denote event types rather than event instances ; that is , they denote a class of events which that are concepts in an upper-level ontology---and our observations , adjectival verbs are verbs that denote event types rather than event instances ; that is , they denote a class of events which that are concepts | 1 |
the viterbi algorithm can not find the best sequences in tolerable response time---viterbi algorithm is the only exact algorithm widely adopted in the nlp applications | 1 |
coreference resolution is the task of grouping mentions to entities---coreference resolution is a key problem in natural language understanding that still escapes reliable solutions | 1 |
in order to do so , we use the moses statistical machine translation toolkit---we use the moses toolkit to train various statistical machine translation systems | 1 |
recursive neural network and convolutional neural network have proven powerful in relation classification---previous applications of recursive neural networks to supervised relation extraction are based on constituency-based parsers | 1 |
we adapted the moses phrase-based decoder to translate word lattices---for phrase-based smt translation , we used the moses decoder and its support training scripts | 1 |
we extend the vector space approach of rapp n -and target phrase e with an m -dimensional vector---we extend the rapp model of context vector projection using a seed lexicon | 1 |
we use the well-known word embedding model that is a robust framework to incorporate word representation features---we use word embeddings 3 as a cheap low-maintenance alternative for knowledge base construction | 1 |
we use the stanford parser to get the basic psts and dts---we use stanford corenlp to obtain dependencies | 1 |
the main goal of dkpro tc is to enable the researcher to quickly find an optimal experimental configuration---the main goal of dkpro tc is to enable researchers to focus on the actual research task behind the learning problem | 1 |
for the optimization process , we apply the diagonal variant of adagrad with mini-batches---to minimize the objective , we use the diagonal variant of adagrad with minibatches | 1 |
then , we trained word embeddings using word2vec---we pre-train the word embeddings using word2vec | 1 |
the ability to automatically predict team performance would be of great value for team training systems---it can be applied as a method for doing automated measurement of team performance | 1 |
zou et al developed a tree kernel-based system to resolve the scope of negation and speculation , which captures the structured information in syntactic parsing trees---recently , bowei et al demonstrated the use of tree kernel based approaches in detecting the scope of negations and speculative sentences using the bioscope corpus | 1 |
we use a binary cross-entropy loss function , and the adam optimizer---in this paper , we address the problem of product aspect rating prediction | 0 |
in this paper , we propose a non-negative matrix factorization based approach to address this issue---in this paper , we study the problem of active dual supervision using non-negative matrix | 1 |
the weights associated to feature functions are optimally combined using the minimum error rate training---all the weights of those features are tuned by using minimal error rate training | 1 |
a moses string-to-tree system is used as our baseline---we build a baseline error correction system , using the moses smt system | 1 |
li et al presented a structured perceptron model to detect triggers and arguments jointly---li et al proposed a joint model to capture the combinational features of triggers and arguments | 1 |
our smt system is a phrase-based system based on the moses smt toolkit---the skip-gram model proposed by mikolov et al has been adapted to the bilingual setting in luong et al , where the model learns to predict word contexts cross-lingually | 0 |
the feature weights 位 m are tuned with minimum error rate training---the parameter weights are optimized with minimum error rate training | 1 |
semantic role labeling ( srl ) is the task of automatically labeling predicates and arguments in a sentence with shallow semantic labels---semantic role labeling ( srl ) is the task of identifying the predicate-argument structure of a sentence | 1 |
the ¡°charniak parser¡± has a labeled precision-recall f-measure of 89.7 % on wsj but a lowly 82.9 % on the test set from the brown corpus treebank---second , the caller ’ s identity may include information that is not typically found in a named entity | 0 |
coreference resolution is the task of clustering referring expressions in a text so that each resulting cluster represents an entity---coreference resolution is a key problem in natural language understanding that still escapes reliable solutions | 1 |
they were again confirmed during the sancl shared task , organized by google , aimed at assessing the performances of parsers on various genres of web texts---we built a 5-gram language model from it with the sri language modeling toolkit | 0 |
the grammar is the general dart of the syntactic box , the part concerned with syntactic structures---the grammar consists of head-dependent relations between words and can be learned automatically from a raw corpus using the reestimation algorithm which is also introduced in this paper | 1 |
carvalho and cohen present a dependency-network based collective classification method to classify email speech acts---carvalho and cohen describe a dependency-network based collective classification method to classify email speech acts | 1 |
supertagging is the tagging process of assigning the correct elementary tree of ltag , or the correct supertag , to each word of an input sentence 1---supertagging is the process of assigning the correct supertag to each word of an input sentence | 1 |
for the source side we use the pos tags from stanford corenlp mapped to universal pos tags---we use stanford corenlp for preprocessing and a supervised learning approach for classification | 1 |
for all experiments , we used a 5-gram english language model trained on the afp and xinua portions of the gigaword v3 corpus with modified kneser-ney smoothing---later work by wang et al was inspired by the similarity between the dependency parse of a sentence and its semantic amr graph | 0 |
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---coreference resolution is a key task in natural language processing ( cite-p-13-1-8 ) aiming to detect the referential expressions ( mentions ) in a text that point to the same entity | 1 |
recent years have witnessed burgeoning development of statistical machine translation research , notably phrase-based and syntax-based approaches---we propose a hybrid model where a seq2seq model and a similarity-based retrieval model are combined to achieve further performance improvement | 0 |
for the semantic language model , we used the srilm package and trained a tri-gram language model with the default goodturing smoothing---we used the sri language modeling toolkit to train a fivegram model with modified kneser-ney smoothing | 1 |
the word embeddings can provide word vector representation that captures semantic and syntactic information of words---it is widely recognized that word embeddings are useful because both syntactic and semantic information of words are well encoded | 1 |
we used the treetagger tool to extract part-of-speech from each given text , then tokenize and lemmatize it---in order to extract useful content words , we first ran part-of-speech tagging and lemmatisation by means of the treetagger tool | 1 |
following spud , crisp is based on a declarative description of the sentence generation problem using tag---crisp uses such algorithms to efficiently solve the sentence generation problem defined by spud | 1 |
therefore , we employ negative sampling and adam to optimize the overall objective function---we use negative sampling to approximate softmax in the objective function | 1 |
mann and yarowsky used semantic information extracted from documents referring to the target person in an hierarchical agglomerative clustering algorithm---sentence planning is a set of interrelated but distinct tasks , one of which is sentence scoping , i.e . the choice of syntactic structure for elementary speech acts and the decision of how to combine them into sentences | 0 |
then we use the standard minimum error-rate training to tune the feature weights to maximize the system潞s bleu score---then we review the path ranking algorithm introduced by lao and cohen | 0 |
the bioscope corpus contains more than 20,000 sentences annotated with speculative and negative key words and their scope---in phrase-structure treebanks , ecs have been used to indicate long-distance dependencies , discontinuous constituents , and certain dropped elements | 0 |
we used minimum error rate training to tune the feature weights for maximum bleu on the development set---recent studies on nlp applications are reported to have good performance applying the pretrained word embedding | 0 |
on the other hand , there are scholars who refuse military-related funding for moral reasons---on a large corpus of noisy and clean sentences , the model is able to generate rich , diverse errors that better capture the noise | 0 |
semantic role labeling ( srl ) is a task of automatically identifying semantic relations between predicate and its related arguments in the sentence---semantic role labeling ( srl ) is the task of labeling the predicate-argument structures of sentences with semantic frames and their roles ( cite-p-18-1-2 , cite-p-18-1-19 ) | 1 |
sentimental sentence constraint is also added for more accurate prediction via another lstm---sentimental sentences , min employs a third lstm for sentimental sentence classification | 1 |
the language models were created using the srilm toolkit on the standard training sections of the ccgbank , with sentenceinitial words uncapitalized---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit from stolcke | 1 |
semantic role labeling ( srl ) is the task of identifying the semantic arguments of a predicate and labeling them with their semantic roles---relation extraction is the task of tagging semantic relations between pairs of entities from free text | 0 |
corpus-based approaches to machine translation have become predominant , with phrase-based statistical machine translation being the most actively progressing area---recent years have witnessed burgeoning development of statistical machine translation research , notably phrase-based and syntax-based approaches | 1 |
for example , jeon et al . have compared the uses of four different retrieval methods , i.e . vector space model , okapi , language model , and translation-based model , within the setting of question search ( cite-p-17-1-10 )---for example , jeon et al . ( cite-p-17-1-9 , cite-p-17-1-10 ) compared four different retrieval methods , i . e . vector space model , okapi , language model ( lm ) , and translation-based model | 1 |
in this shared task , we employ the word embeddings model to reflect paradigmatic relationships between words---in this approach we are attempting to identify the importance of neural word embeddings to accurately capture the context of the main keywords of the abstracts | 1 |
the techniques use spelling expansion , morphological expansion , dictionary term expansion and proper name transliteration to reuse or extend a phrase table---the techniques use morphological expansion ( m orph e x ) , spelling expansion ( s pell e x ) , dictionary word expansion ( d ict e x ) and proper name transliteration ( t rans e x ) to reuse or extend phrase tables | 1 |
semantic role labeling ( srl ) is the task of identifying semantic arguments of predicates in text---semantic role labeling ( srl ) is the process of assigning semantic roles to strings of words in a sentence according to their relationship to the semantic predicates expressed in the sentence | 1 |
luong et al segment words using morfessor , and use recursive neural networks to build word embeddings from morph embeddings---in , the authors use a recursive neural network to explicitly model the morphological structures of words and learn morphologically-aware embeddings | 1 |
most interestingly , a qualitative analysis zoomed into the assignment behaviour of the soft clustering approaches , and revealed different attitudes towards predicting ambiguity---soft clustering approaches are required for the task but reveal quite different attitudes towards predicting ambiguity | 1 |
they use ngram features such as unigrams and bigrams---they use features such as unigrams and bigrams | 1 |
for instance , the dirt system uses the mutual information between the argument pairs for two binary relations to measure the similarity between them , and clusters relations accordingly---part-of-speech ( pos ) tagging is a fundamental nlp task , used by a wide variety of applications | 0 |
a 5-gram language model with kneser-ney smoothing is trained using s-rilm on the target language---we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus | 1 |
the simplest models compute a phrase vector by adding the vectors for the individual words or by a component-wise product of word vectors---word sense disambiguation ( wsd ) is a natural language processing ( nlp ) task in which the correct meaning ( sense ) of a word in a given context is to be determined | 0 |
we also present a novel baseline that performs remarkably well without using topic identification---in this paper , we propose a joint learning method of two smt systems for paraphrase generation | 0 |
in this paper , we present an implicit content-introducing method for generative conversation systems , which incorporates cue words using our proposed hierarchical gated fusion unit ( hgfu ) in a flexible way---unlike the existing work , we explore an implicit content-introducing method for neural conversation systems , which utilizes the additional cue word in a “ soft ” manner | 1 |
for part of speech tagging and dependency parsing of the text , we used the toolset from stanford corenlp---for preprocessing , we used corenlp to automatically parse the raw text of wsj for feature extraction | 1 |
izumi et al proposed a maximum entropy model , using lexical and pos features , to recognize a variety of errors , including verb form errors---izumi et al was the first to evaluate a model trained on incorrect usage as well as artificial errors for the task of correcting several different error types , including prepositions | 1 |
their weights are optimized using minimum error-rate training on a held-out development set for each of the experiments---coreference resolution is the problem of identifying which noun phrases ( nps , or mentions ) refer to the same real-world entity in a text or dialogue | 0 |
we trained a 4-gram language model on the xinhua portion of gigaword corpus using the sri language modeling toolkit with modified kneser-ney smoothing---we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit | 1 |
we use pre-trained word vectors of glove for twitter as our word embedding---tan et al employ social relationships to improve user-level twitter sentiment analysis | 0 |
le and mikolov extended the word embedding learning model by incorporating paragraph information---le and mikolov presented the paragraph vector in sentiment analysis | 1 |
our model achieves the best results to date on the kbp 2016 english and chinese datasets---recently , question generation has got immense attention from the researchers and hence , different methods have been proposed to accomplish the task in different relevant fields | 0 |
bilingual lexicon induction is the task of finding words that share a common meaning across different languages---sentiment analysis is the process of identifying and extracting subjective information using natural language processing ( nlp ) | 0 |
in , a slightly enhanced version of osm was integrated into the log-linear framework of the moses system---the moses smt system allows for the use of user-defined features in its loglinear model | 1 |
in extending their work , the pagerank algorithm is applied to rank senses in terms of how strongly they are positive or negative---they then extend their work by applying the page rank algorithm to ranking the wordnet senses in terms of how strongly a sense possesses a given semantic property | 1 |
we use minimal error rate training to maximize bleu on the complete development data---in general , we could get the optimized parameters though minimum error rate training on the development set | 1 |
for both languages , we used the srilm toolkit to train a 5-gram language model using all monolingual data provided---collins and singer present a variant of the blum and mitchell algorithm , which directly maximises an objective function that is based on the level of agreement between the classifiers on unlabelled data | 0 |
ikeda et al first proposed a machine learning approach to detect polarity shifting for sentencelevel sentiment classification---ikeda et al proposed a method that classifies polarities by learning them within a window around a word | 1 |
twitter is a microblogging service that has 313 million monthly active users 1---in this paper , we are interested in considering the term dependence to improve the answer reranking for definitional | 0 |
the tool allows users to search tree expressions with a given tree query---by using the proposed tool , users develop tree structure patterns through abstracting syntax | 1 |
we use the moses statistical mt toolkit to perform the translation---we develop translation models using the phrase-based moses smt system | 1 |
automatic semantic role labeling was first introduced by gildea and jurafsky---early work in frame-semantic analysis was pioneered by gildea and jurafsky | 1 |
frermann et al present a bayesian generative model for joint learning of event types and ordering constraints---hochreiter and schmidhuber proposed long short-term memories as the specific version of rnn designed to overcome vanishing and exploding gradient problem | 0 |
in contrast , human feedback has a positive and statistically significant , but lower , impact on precision and recall---in contrast , human feedback has relatively small impact on precision and recall | 1 |
while many idioms do have these properties , all idioms fall on the continuum from being compositional to being partly unanalyzable to completely non-compositional---finally , mead is a widely used multi-document summarization and evaluation platform | 0 |
however , these studies do not address the relationship between melody and the discourse structure of lyrics---while these studies provide insightful findings on the properties of lyrics , none of those takes the approach of using melody-lyrics | 1 |
recurrent neural network architectures have proven to be well suited for many natural language generation tasks---using recurrent neural networks has become a very common technique for various nlp based tasks like language modeling | 1 |
however , in this paper we focus on identity type of relationships only---ganin and lempitsky presented an adversarial approach to domain adaptation for transferring knowledge from source domain to target domains | 0 |
for the semantic language model , we used the srilm package and trained a tri-gram language model with the default goodturing smoothing---we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
we used the 300-dimensional glove word embeddings learned from 840 billion tokens in the web crawl data , as general word embeddings---for word embedding , we used pre-trained glove word vectors with 300 dimensions , and froze them during training | 1 |
word sense disambiguation ( wsd ) is formally defined as the task of computationally identifying senses of a word in a context---word sense disambiguation ( wsd ) is a fundamental task and long-standing challenge in natural language processing ( nlp ) | 1 |
named entity recognition ( ner ) is a challenging learning problem---named entity recognition ( ner ) is a fundamental task in text mining and natural language understanding | 1 |
this , we believe , is an important step toward understanding how mtl works---dong et al represents questions using three cnns with different parameters when dealing with different answer aspects including answer path , answer context and answer type | 0 |
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