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for example , socher et al exploited tensor-based function in the task of sentiment analysis to capture more semantic information from constituents---for example , socher et al demonstrates that sentiment analysis , which is usually approached as a flat classification task , can be viewed as tree-structured | 1 |
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---coreference resolution is the task of determining which mentions in a text refer to the same entity | 1 |
minimum error rate training is an iterative procedure for training a log-linear statistical machine translation model---minimum error rate training is a stochastic optimization algorithm that typically finds a different weight vector each time it is run | 1 |
dave et al discuss the major structural divergences with respect to english and hindi---following this research direction , in this work , we explore the use of ecoc to enhance the performance of centroid classifier | 0 |
an annotation effort shows implicit relations boost the amount of meaning explicitly encoded for verbs---an annotation effort demonstrates implicit relations reveal as much as 30 % of meaning | 1 |
the translation quality is evaluated by bleu and ribes---distributional semantic models encode word meaning by counting co-occurrences with other words within a context window and recording these counts in a vector | 0 |
word embeddings have shown promising results in nlp tasks , such as named entity recognition , sentiment analysis or parsing---distributed representations for words and sentences have been shown to significantly boost the performance of a nlp system | 1 |
the system applies transformation rules to a typed dependency representation obtained from the stanford parser---the stanford parser can output typed semantic dependencies that conform to the stanford dependencies | 1 |
named entity recognition ( ner ) is the task of finding rigid designators as they appear in free text and classifying them into coarse categories such as person or location ( cite-p-24-4-6 )---named entity recognition ( ner ) is the task of identifying and typing phrases that contain the names of persons , organizations , locations , and so on | 1 |
the language model was constructed using the srilm toolkit with interpolated kneser-ney discounting---the language model is a 5-gram with interpolation and kneser-ney smoothing | 1 |
we used stanford corenlp to generate dependencies for the english data---we used a caseless parsing model of the stanford parser for a dependency representation of the messages | 1 |
sequences of words which exhibit a cohesive relationship are called lexical chains ( cite-p-11-3-8 )---lexical chains are defined as groups of semantically related words that represent the lexical cohesive structure of a text e.g . { flower , petal , rose , garden , tree } | 1 |
in addition , the final results of our joint methods are comparable to representative existing methods despite using no external resources---our methods are superior to a strong baseline and comparable to the methods of representative previous work | 1 |
in this paper , we propose a novel task , text recap extraction---in this paper , we explore a new problem of text recap extraction | 1 |
we also use early stopping based on the performance achieved on the development sets---in order to prevent overfitting , we used early stopping based on the performance on the development set | 1 |
the word embeddings are pre-trained by skip-gram---the most commonly used word embeddings were word2vec and glove | 1 |
in this paper , we present a new algorithm for geo-centric language model generation for local business voice search for mobile users---in this paper , we present an efficient algorithm for constructing geo-centric language models from a business listing database and local business search | 1 |
essk is the simple extension of the word sequence kernel and string subsequence kernel---commonly used kernels in nlp are string kernels and tree kernels | 1 |
to train our models , we use svm-light-tk 15 , which enables the use of structural kernels in svm-light---the actual implementations we use for training are the svm-light-tk package , which is a tree kernel extension to svm light | 1 |
we use a frame based parser similar to the dypar parser used by carbonell , et al to process ill-formed text , semantic information is represented in a set of frames---we use a frame based parser similar to the dypar parser used by carbonell , et al to process ill-formed text , semantic information is represented by a set of frames | 1 |
here , we choose the skip-gram model and continuous-bag-of-words model for comparison with the lbl model---to start with , we replace word types with corresponding neural language model representations estimated using the skip-gram model | 1 |
for the mix one , we also train word embeddings of dimension 50 using glove---we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings | 1 |
our baseline system is based on a hierarchical phrase-based translation model , which can formally be described as a synchronous context-free grammar---lkb system is a parser generation tool , proposed by | 0 |
riloff and wiebe learned the extraction patterns for subjective expressions---riloff and wiebe extracted subjective expressions from sentences using a bootstrapping pattern learning process | 1 |
the irstlm toolkit is used to build ngram language models with modified kneser-ney smoothing---we use srilm to build 5-gram language models with modified kneser-ney smoothing | 1 |
while simple and principled , our model achieves performance competitive with a state-of-the-art ensemble system combining latent semantic representations and surface similarity---using this principled latent variable model alone , we achieve the performance competitive with a state-of-the-art method which combines a latent space | 1 |
sentiment analysis is the natural language processing ( nlp ) task dealing with the detection and classification of sentiments in texts---sentiment analysis is the study of the subjectivity and polarity ( positive vs. negative ) of a text ( cite-p-7-1-10 ) | 1 |
in this paper , we propose a different method for nsw detection---in the following , we call this task the nsw detection task | 1 |
this paper derives the conditions under which a given probabilistic tag can be shown to be consistent---in this paper the conditions under which a given probabilistic tag can be shown to be consistent | 1 |
in this work , we propose to exploit argument information explicitly for ed via supervised attention mechanisms---in this paper is a novel unified way to directly optimize the search phase of query spelling correction | 0 |
the pos tags used in the reordering model are obtained using the treetagger---the rules were extracted using the pos tags generated by the treetagger | 1 |
we use online learning to train model parameters , updating the parameters using the adagrad algorithm---we apply online training , where model parameters are optimized by using adagrad | 1 |
commonly applied models include hidden markov models , maximum entropy markov models , and conditional random fields---examples of these models include maximum entropy markov models , bayesian information extraction network , and conditional random fields | 1 |
we use the mert algorithm for tuning and bleu as our evaluation metric---we evaluate the translation quality using the case-sensitive bleu-4 metric | 1 |
we build on prior work by adding partial supervision from verbnet , treating verbnet classes as additional latent variables---like verbnet , we expand a dirichlet process mixture model to predict a verbnet class | 1 |
we use the automatic mt evaluation metrics bleu , meteor , and ter , to evaluate the absolute translation quality obtained---we use the pre-trained word2vec embeddings provided by mikolov et al as model input | 0 |
a lexicalized reordering model was trained with the msd-bidirectional-fe option---the lexical reordering model introduced in was integrated into phrase-based decoding | 1 |
in addition , we reveal an interesting finding that the earth mover ’ s distance shows potential as a measure of language difference---as an interesting byproduct , the earth mover ’ s distance provides a distance measure that may quantify a facet of language difference | 1 |
with enough data in the training set , even infrequent verbs have sufficient data to support learning---in the training set , even infrequent verbs have sufficient data to support learning | 1 |
cite-p-15-1-5 proposed an automatic evaluation method using multiple evaluation results from a manual method---cite-p-15-1-13 proposed an automatic method that gives an evaluation result of a translation system | 1 |
word sense disambiguation ( wsd ) is a task to identify the intended sense of a word based on its context---culotta and sorensen , 2004 ) extended this work to calculate kernels between augmented dependency trees | 0 |
a common approach to the automatic extraction of semantically related words is to use distributional similarity---the system automatically generates a thesaurus using a measure of distributional similarity and an untagged corpus | 0 |
we have used the srilm with kneser-ney smoothing for training a language model of order five and mert for tuning the model with development data---we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing | 1 |
we use several classifiers including logistic regression , random forest and adaboost implemented in scikit-learn---we use the logistic regression implementation of liblinear wrapped by the scikit-learn library | 1 |
in this article , we propose a temporal sense clustering algorithm based on the idea that semantically related hashtags have similar and synchronous usage patterns---in this article is to use an algorithm for hashtag sense clustering based on temporal co-occurrence and similarity | 1 |
in the prototypical instance of this class , word-sense disambiguation , such distinct semantic concepts as river bank , financial bank and to bank an airplane are conflated in ordinary text---in the prototypical instance of this class , word-sense disambiguation , such distinct semantic concepts as river bank , financial bank and to bank | 1 |
the feature weights for each system were tuned on development sets using the moses implementation of minimum error rate training---we used the phrasebased smt system moses to calculate the smt score and to produce hfe sentences | 0 |
translation quality is evaluated by case-insensitive bleu-4 metric---the translation quality is evaluated by caseinsensitive bleu-4 metric | 1 |
in this paper , we propose a joint learning method of two smt systems to optimize the process of paraphrase generation---in this paper , we propose a joint learning method of two smt systems for paraphrase generation | 1 |
for bi we use 2-gram kenlm models trained on the source training data for each domain---for all the systems we train , we build n-gram language model with modified kneserney smoothing using kenlm | 1 |
beam-search has been applied to transition-based dependency parsing in recent studies---transition-based methods have given competitive accuracies and efficiencies for dependency parsing | 1 |
le and mikolov extended the word embedding learning model by incorporating paragraph information---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 |
the 4-gram language model was trained with the kenlm toolkit on the english side of the training data and the english wikipedia articles---a 5-gram language model on the english side of the training data was trained with the kenlm toolkit | 1 |
zeng et al proposed an approach for relation classification where sentence-level features are learned through a cnn , which has word embedding and position features as its input---zeng et al proposed a cnn network integrating with position embeddings to make up for the shortcomings of cnn missing contextual information | 1 |
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 natural language processing ( nlp ) task ( cite-p-10-3-0 ) which aims at classifying documents according to the opinion expressed about a given subject ( federici and dragoni , 2016a , b ) | 1 |
relation extraction is the task of recognizing and extracting relations between entities or concepts in texts---relation extraction ( re ) is the task of recognizing the assertion of a particular relationship between two or more entities in text | 1 |
table 1 shows the translation performance by bleu---table 4 shows translation results in terms of bleu , ribes , and ter | 1 |
performance is measured using bleu , meteor , and ter---lsa has remained a popular approach for asag and been applied in many variations | 0 |
the srilm toolkit was used to build the 5-gram language model---to test this hypothesis , we use the rocchio algorithm | 0 |
titov and mcdonald underline the need for unsupervised methods for aspect detection---titov and mcdonald emphasize the importance of an unsupervised approach for aspect detection | 1 |
the graph is constructed from the similarity matrix---the similarity matrix needs to be calculated | 1 |
our experiments use the ghkm-based string-totree pipeline implemented in moses---as an alternative to this operationally defined rewriting view of adjunction , vijay-shanker suggests that tag derivations instead be viewed as a monotonic growth of structural assertions that characterize the structures being composed | 0 |
coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world---coreference resolution is the task of grouping mentions to entities | 1 |
event coreference resolution is the task of determining which event mentions expressed in language refer to the same real-world event instances---more importantly , event coreference resolution is a necessary component in any reasonable , broadly applicable computational model of natural language understanding ( cite-p-18-3-4 ) | 1 |
in the experiments we trained 5-gram language models on the monolingual parts of the bilingual corpora using srilm---for all experiments , we used a 4-gram language model with modified kneser-ney smoothing which was trained with the srilm toolkit | 1 |
broad coverage and disambiguation quality are critical for a word sense disambiguation system---broad coverage and disambiguation quality are critical for wsd | 1 |
gram language models are trained over the target-side of the training data , using srilm with modified kneser-ney discounting---this paper describes our investigation into the effectiveness of lexicalization in dependency parsing | 0 |
the system suggests full-sentence extensions of the current translation prefix---in the following way : the system suggests an extension of the current translation prefix | 1 |
we use moses toolkit for pbsmt training and sockeye toolkit for nmt training---to dependency structures , transition systems are also helpful for amr parsing | 0 |
however , there are a number of caveats which must be considered which we discuss subsequently---we also discuss a number of caveats which must be kept in mind | 1 |
the features are inspired by saloj盲rvi et al who used a similar exploratory approach---the features are inspired by saloj盲rvi et al , who used a similarly exploratory approach | 1 |
luhn uses frequency to weight content words and extracts sentences with the highest combined content scores to form the summary---luhn uses frequency to weight content words and extracts sentences with the highest combined content scores | 1 |
a dialogue system is usually defined as a computer system that can interact with a human being through dialogue in order to complete a specific task ( e.g. , ticket reservation , timetable consultation , bank operations , . . . ) ( cite-p-10-5-2 , cite-p-10-5-9 )---a dialogue system is a program in which a user and system communicate in natural language | 1 |
for word-level embeddings , we pre-train the word vectors using word2vec on the gigaword corpus mentioned in section 4 , and the text of the training dataset---for the embeddings trained on stack overflow corpus , we use the word2vec implementation of gensim 8 toolkit | 1 |
the weights associated to feature functions are optimally combined using the minimum error rate training---we utilize minimum error rate training to optimize feature weights of the paraphrasing model according to ndcg | 1 |
convolutional neural networks ( cnns ) have shown to yield very strong results in several computer vision tasks---convolutional networks have been successfully applied in image classification and understanding | 1 |
to learn the topics we use latent dirichlet allocation---we train a 4-gram language model on the xinhua portion of the gigaword corpus using the sri language toolkit with modified kneser-ney smoothing | 0 |
to extract terms we used lingua english tagger for finding single and multi-token nouns and the stanford named entity recognizer to extract named entities---for part-of-speech and named entity tags , we used the stanford log-linear part-ofspeech tagger and the stanford named entity recognizer | 1 |
we use word2vec as the vector representation of the words in tweets---for efficiency , we follow the hierarchical softmax optimization used in word2vec | 1 |
grenager et al , 2005 , used a first order hmm which has a diagonal transition matrix and a specialized boundary model---grenager et al , 2005 ) presents an unsupervised hmm based on the observation that the segmented fields tend to be of multiple words length | 1 |
optimization on a more diverse data set showed better performance---ensemble methods have shown the best performance | 1 |
in this paper , we propose a modular approach for the semeval-2010 task on chinese event detection---in this paper , we describe the system submitted to the semeval-2010 task 11 on event detection | 1 |
experimental results over evaluation sets of noun phrases from multiple sources demonstrate that interpretations extracted from queries have encouraging coverage and precision---erkan and radev introduced a stochastic graph-based method , lexrank , for computing the relative importance of textual units for multi-document summarization | 0 |
we also propose an rnn based approach for generating natural language questions from an input keyword sequence---in a knowledge graph , we train the rnn model for generating natural language questions from a sequence of keywords | 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 multiple mentions that refer to the same entity | 1 |
semantic parsing is the task of converting a sentence into a representation of its meaning , usually in a logical form grounded in the symbols of some fixed ontology or relational database ( cite-p-21-3-3 , cite-p-21-3-4 , cite-p-21-1-11 )---semantic parsing is the task of converting natural language utterances into formal representations of their meaning | 1 |
we propose an opinion retrieval model based on hits , a popular graph ranking algorithm---we propose another opinion sentence ranking model based on the popular graph ranking algorithm hits | 1 |
we use a maximum entropy classifier with a large number of boolean features , some of which are novel---we use the mallet implementation of a maximum entropy classifier to construct our models | 1 |
morante and daelemans and ozg眉r and radev propose scope detectors using the bioscope corpus---among many others , morante and daelemans and li et al propose scope detectors using the bioscope corpus | 1 |
blitzer et al induced a correspondence between features from a source and target domain based on structural correspondence learning over unlabelled target domain data---sentiment analysis ( sa ) is the determination of the polarity of a piece of text ( positive , negative , neutral ) | 0 |
we use the 300-dimensional pre-trained word2vec 3 word embeddings and compare the performance with that of glove 4 embeddings---we use different pretrained word embeddings such as glove 1 and fasttext 2 as the initial word embeddings | 1 |
in the opinion sentiment slot , we used a 3 class polarity classifier , having bow , lemmas , bigrams after verbs , presence of polarized terms , and punctuation based features---for the sentiment polarity slot , we used a a supervised machine learning classifier , having bag-of-words ( bow ) , lemmas , bigrams after | 1 |
in this paper , we address target-dependent sentiment classification of tweets---in this paper , we propose to improve target-dependent twitter sentiment classification | 1 |
moreover , a bilingual cue expansion method is proposed to increase the coverage in cue detection---cue expansion strategy is proposed to increase the coverage in cue detection | 1 |
when labeled training data is available , we can use the maximum entropy principle to optimize the 位 weights---to implement the twin model , we adopt the log linear or maximum entropy model for its flexibility of combining diverse sources of information | 1 |
it turns out that the compositional account is more complex on this measure---in all cases turns out to be the same , the compositional approach is more complex | 1 |
in this paper , we have presented an ensemble network of deep learning and classical feature driven models---in this paper , we propose a novel multi-layer perceptron ( mlp ) based ensemble | 1 |
text summarization is to produce a brief summary of the main ideas of the text---summarization is to produce a brief summary of the main ideas of the text | 1 |
for german , the pos and morphological tags were obtained from rftagger which provides morphological information such as case , number and gender for nouns and tense for verbs---1 the atb comprises manually annotated morphological and syntactic analyses of newswire text from different arabic sources | 0 |
following mintz et al , we carried out our experiments using wikipedia as the target corpus and freebase as the knowledge base---our work was inspired by mintz et al who used freebase as a knowledge base by making the ds assumption and trained relation extractors on wikipedia | 1 |
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