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we use 300d glove vectors trained on 840b tokens as the word embedding input to the lstm---in this task , we use the 300-dimensional 840b glove word embeddings | 1 |
there is a large literature on document classification and automated text categorization---there has been a great deal of research on text classification , which most commonly has used bag-of-word features | 1 |
in our pilot study , the use of approximate-polynomial kernel ( cite-p-13-5-0 ) outperforms the linear kernel svm in chinese and arabic---to improve performance , we perform an automated search for optimal values and show that suboptimal parameter selection can significantly decrease performance | 0 |
in this paper , we develop an adaptive topic model to go beyond a strictly sequential model while allow some hierarchical influence---sentiment analysis is a nlp task that deals with extraction of opinion from a piece of text on a topic | 0 |
we report meteor and sentence level bleu-4 scores---we report bleu and ter evaluation scores | 1 |
all of these options are compatible with our algorithm---grouping hypotheses by these similar words enables our algorithm | 1 |
choi et al , showed how to enhance chinese-english verb alignments by exploring predicate-argument structure alignment using parallel propbanks---choi et al showed how to enhance chinese-english verb alignments by exploring predicate-argument structure alignment using parallel propbanks | 1 |
for the feature-based system we used logistic regression classifier from the scikit-learn library---we use several classifiers including logistic regression , random forest and adaboost implemented in scikit-learn | 1 |
inspired by this observation , we propose a simple sentence selector to select the minimal set of sentences to feed into the qa model---second , inspired by this observation , we proposed a sentence selector which selects a minimal set of sentences | 1 |
these preference rules can be incorporated into a polynomial time generation algorithm , while some alternative formalizations of conversational impficature make the generation task np-hard---the model parameters in word embedding are pretrained using glove | 0 |
within this subpart of our ensemble model , we used a svm model from the scikit-learn library---we used the scikit-learn implementation of a logistic regression model using the default parameters | 1 |
large context windows , we studied the relatedness and similarity subsets of the popular wordsim-353 reference dataset---to assess the overall quality of proposed embedding method , we examined its performance via the word similarity task on simlex-999 , wordsim-353 , and men datasets | 1 |
mishne and de rijke , 2006 ) constructed models to predict the levels of various moods according to the language used by bloggers at a giv-en time---mishne and de rijke , 2006 ) collect user-labeled mood from blog post text on livejournal and exploit them for predicting the intensity of moods over a time span rather than at the post level | 1 |
particularly , in this paper we focus on clustering methods for grouping sentences in an article that discuss the same event---in this paper , we investigate the use of clustering methods for the task of grouping | 1 |
other terms used in the literature include implied meanings , implied alternatives and semantically similar---mcclosky et al presented a self-training method combined with a reranking algorithm for constituency parsing | 0 |
for all classifiers , we used the scikit-learn implementation---in this section , we briefly review the research works of sentiment analysis in twitter | 0 |
we apply it here to information structure analysis of scientific documents---we focus on the analysis of the information structure ( is ) of scientific articles | 1 |
we use the glove vectors of 300 dimension to represent the input words---we use 300-dimensional word embeddings from glove to initialize the model | 1 |
it has already shown promising results in computational biology dinu et al , 2014 ) and native language identification---local rank distance has already shown promising results in computational biology dinu et al , 2014 ) and native language identification | 1 |
agirre et al demonstrated that semantic classes obtained from english wordnet help to obtain significant improvements in both pp attachment and pcfg parsing---for instance , agirre et al demonstrate that using wordnet semantic classes benefits pp attachment performance | 1 |
twitter is a huge microbloging service with more than 500 million tweets per day 1 from different locations in the world and in different languages---twitter is a communication platform which combines sms , instant messages and social networks | 1 |
we prove this on the task of judging event coreference---we employed the task of event coreference | 1 |
lampos et al use word embedding for enriching the feature selection of the flu model and thereby increase the inference performance---djuric et al leveraged word embedding representations to improve machine learning based classifiers | 1 |
irony is a form of figurative language , considered as “ saying the opposite of what you mean ” , where the opposition of literal and intended meanings is very clear ( cite-p-23-1-1 , cite-p-23-3-8 )---irony is a complex linguistic phenomenon widely studied in philosophy and linguistics ( cite-p-14-3-1 , cite-p-14-3-19 , cite-p-14-3-25 ) | 1 |
in this paper , we do model adaptation using a neural network framework---the feature weights 位 i are trained in concert with the lm weight via minimum error rate training | 0 |
our translation model is implemented as an n-gram model of operations using the srilm toolkit with kneser-ney smoothing---we use a fourgram language model with modified kneser-ney smoothing as implemented in the srilm toolkit | 1 |
in this paper we present a formal computational framework for modeling manipulation actions---in this paper , we propose an approach for learning the semantic meaning of manipulation action | 1 |
senserelate : :targetword is a perl package that implements this algorithm---senserelate : :targetword is a perl package that implements these ideas , and is able to disambiguate a target word in context by finding the sense that is most related to its neighbors according to a specified measure | 1 |
semantic parsing is the problem of deriving a structured meaning representation from a natural language utterance---semantic parsing is the task of converting natural language utterances into formal representations of their meaning | 1 |
classifier we use the l2-regularized logistic regression from the liblinear package , which we accessed through weka---we use the wrapper of the scikit learn python library over the liblinear logistic regression implementation | 1 |
parameters were tuned using minimum error rate training---tuning of models used minimum error rate training , repeated 3 times and averaged | 1 |
the hmm is a generative modeling approach since it describes a stochastic process with hidden variables ( sentence boundary ) that produces the observable data---an hmm is a generative model , yet it is able to model the sequence via the forward-backward algorithm | 1 |
prettenhofer and stein provided a cl-scl model based on structural correspondence learning for sentiment classification---prettenhofer and stein proposed a cross-language structural correspondence learning method to induce language-independent features by using word translation oracles | 1 |
berkeley parser is adopted to obtain the constituent parse tree for every sentence and pos tag for every token---berkeley parser is used to get the constituent parse tree for every sentence | 1 |
for vpe detection , we improve upon the accuracy of the state-of-the-art system by over 11 % , from 69.52 % to 80.78 %---the neural network for greedy training is based on the neural networks of chen and manning and | 0 |
word sense disambiguation ( wsd ) is a particular problem of computational linguistics which consists in determining the correct sense for a given ambiguous word---in this paper , we propose a new generative approach for semantic slot filling task | 0 |
only henrich and hinrichs enrich the output of morphological segmentation with information from the annotated compounds of germanet to disambiguate such structures---only henrich and hinrichs enrich the output of morphological segmentation with information from germanet to disambiguate such structures | 1 |
marcu and echihabi proposed a method for cheap acquisition of training data for discourse relation sense prediction---marcu and echihabi presented an unsupervised method to recognize discourse relations held between arbitrary spans of text | 1 |
an effective solution for these problems is the long short-term memory architecture---the lstm architecture is proposed to address this problem | 1 |
in this paper , we explored the use of location information ( from gps or cell tower triangulation ) to improve asr accuracy in lbvs---in this paper , we present a new algorithm for geo-centric language model generation for local business | 1 |
for evaluation of machine translation quality , standard automatic evaluation metrics are used , like bleu and ribes in all experiments---for the evaluation of machine translation quality , some standard automatic evaluation metrics have been used , like bleu , nist and ribes in all experiments | 1 |
second , we use a bridging operation to generate additional predicates based on neighboring predicates---event extraction is a particularly challenging problem in information extraction | 0 |
recently , lin showed that statistical sentence-shortening approaches like knight and marcu do not improve content selection in summaries---in natural language , a word often assumes different meanings , and the task of determining the correct meaning , or sense , of a word in different contexts is known as word sense disambiguation ( wsd ) | 0 |
the abstract meaning representation is a semantic meaning representation language that is purposefully syntax-agnostic---abstract meaning representation is a framework suitable for integrated semantic annotation | 1 |
we use the moses phrase-based mt system with standard features---our baseline is a standard phrase-based smt system | 1 |
early translation retrieval methods were widely used in example-based and memory-based translation systems---translation retrieval is firstly introduced in translation memory systems | 1 |
we adopted dependency structure as the context of words since it is the most widely used and wellperforming contextual information in the past studies---we used a dependency structure as the context for words because it is the most widely used and one of the best performing contextual information in the past studies | 1 |
we used a dependency structure as the context for words because it is the most widely used and one of the best performing contextual information in the past studies---in deployed dialog systems with real users , as in laboratory experiments , users adapt to the system ’ s lexical and syntactic choices | 0 |
relation extraction ( re ) is the task of determining semantic relations between entities mentioned in text---for all the systems we train , we build n-gram language model with modified kneserney smoothing using kenlm | 0 |
to evaluate our model , we develop an annotated corpus based on microblogs---in this study , we investigate and analyze three different approaches | 0 |
our baseline parser uses the feature set described by zhang and nivre---table 4 show the feature templates of our parser , most of which are based on those of zhang and nivre | 1 |
opinion mining ( or sentiment analysis ) has attracted a great deal of attention from researchers of natural language processing and data mining in the past few years due to many challenging research problems and practical applications---analysis of opinions , known as opinion mining or sentiment analysis , has attracted a great deal of attention recently due to many practical applications and challenging research problems | 1 |
lexical resources like wordnet which are proved to be of great help for wsd in the knowledge-based methods---lexical resources like wordnet ( cite-p-24-3-11 ) which are widely used in the knowledge-based methods | 1 |
twitter is a rich resource for information about everyday events – people post their tweets to twitter publicly in real-time as they conduct their activities throughout the day , resulting in a significant amount of mundane information about common events---experiments on deep parsing of penn treebank have been reported for combinatory categorial grammar and lexical functional grammar | 0 |
in a language generation system , a content planner embodies one or more “ plans ” that are usually hand–crafted , sometimes through manual analysis of target text---in a language generation system , a content planner typically uses one or more “ plans ” to represent the content to be included in the output | 1 |
in this paper , we propose a simple , yet effective method to incorporate discrete , probabilistic lexicons as an additional information source in nmt ( ¡ì3 )---in this paper , we have proposed a method to incorporate discrete probabilistic lexicons into nmt systems | 1 |
further , we importantly demonstrate that this task gives us reasonable results even when modeled as a semi-supervised problem---in community questions , we propose to treat the question subject as the primary part of the question , and aggregate the question body information based on similarity and disparity with the question subject | 0 |
peng and mccallum , 2004 ) proposed oov word extraction methods based on crf-based word segmenter---in this paper we present a text-to-text rewriting model that scales to non-isomorphic cases | 0 |
by imposing a composite ` 1 , ¡þ regularizer , we obtain structured sparsity , driving entire rows of coefficients to zero---we apply a composite regularizer that drives entire rows of the coefficient matrix to zero , yielding compact , interpretable models | 1 |
our first evaluation exercise was based on a random sample text from a technical manual in english---the first evaluation exercise for english was based on a random sample text from a technical manual | 1 |
lauer and subsequent studies demonstrate that the dependency model performs better than the adjacency model---lauer has shown that the dependency models perform better than the adjacency models | 1 |
shen et al proposed a string-to-dependency target language model to capture long distance word orders---again , shen et al explore a dependency language model to improve translation quality | 1 |
semantic role labeling ( srl ) has been defined as a sentence-level natural-language processing task in which semantic roles are assigned to the syntactic arguments of a predicate ( cite-p-14-1-7 )---semantic role labeling ( srl ) is a task of analyzing predicate-argument structures in texts | 1 |
recently , distant supervision has emerged to be a popular choice for training relation extractors without using manually labeled data---a recent approach for training information extraction systems is distant supervision , which exploits existing knowledge bases instead of annotated texts as the source of supervision | 1 |
peters et al show how deep contextualized word representations model both complex characteristics of word use , and usage across various linguistic contexts---semantic parsing is the task of mapping natural language utterances to machine interpretable meaning representations | 0 |
semantic role labeling ( srl ) is the task of identifying the semantic arguments of a predicate and labeling them with their semantic roles---we extract all word pairs which occur as 1-to-1 alignments and later refer to them as a list of word pairs | 0 |
we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting---we used the sri language modeling toolkit to train a fivegram model with modified kneser-ney smoothing | 1 |
in our word embedding training , we use the word2vec implementation of skip-gram---our both intrinsic and extrinsic experiments demonstrated that online-pmi algorithm | 0 |
we present a new cross-lingual task for semeval concerning the translation of l1 fragments in an l2 context---however , s-lstm models hierarchical encoding of sentence structure as a recurrent state | 0 |
bengio et al presented a neural network language model where word embeddings are simultaneously learned along with a language model---we utilize a maximum entropy model to design the basic classifier for wsd and tc tasks | 0 |
the weights of the word embeddings use the 300-dimensional glove embeddings pre-trained on common crawl data---the word embeddings are initialized with the publicly available word vectors trained through glove 5 and updated through back propagation | 1 |
coster and kauchak and wubben et al use a modified phrase-based model based on a machine translation framework---wubben et al and coster and kauchak apply phrase based machine translation to the task of text simplification | 1 |
therefore , word segmentation is a preliminary and important preprocess for chinese language processing---word segmentation is the foremost obligatory task in almost all the nlp applications where the initial phase requires tokenization of input into words | 1 |
in this section , we present a real-world application of the al+ ener api : glossary linking in an online news service---we describe an application of the api for automatic extraction of glossaries in a japanese online news service | 1 |
a back-off 2-gram model with good-turing discounting and no lexical classes was also created from the training set , using the srilm toolkit ,---we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings | 0 |
it also has been found that each of the english equivalent synsets occurs in each separate class of english verbnet---it is found that each of the english equivalent synsets occurs in each separate class of english verbnet | 1 |
the human-annotated labels that accompany media on flickr enable us to acquire predicate-argument co-occurrence information---language model scores , along with other features , are used in a maxent reranker to identify the most plausible analysis | 0 |
we also show that this method appears robust in the face of off-topic dialogue and speech recognition errors---accuracy is robust in the face of noise , both in the form of off-topic discussion and speech recognition | 1 |
dependency parsing is the task of building dependency links between words in a sentence , which has recently gained a wide interest in the natural language processing community---for example , dirt aims to discover different representations of the same semantic relation using distributional similarity of dependency paths | 0 |
for example , yu and dredze proposed a method to employ graph knowledge to improve word embedding , and used text data to assist new relation discovery for graph knowledge bases---for example , yu and dredze have proposed a new learning objective function to enhance word embeddings by combining neural models and a prior knowledge measure from semantic resources | 1 |
we evaluated the translation quality of the system using the bleu metric---we use case-sensitive bleu-4 to measure the quality of translation result | 1 |
sarcasm is defined as ‘ the use of irony to mock or convey contempt ’ 1---sarcasm , commonly defined as ‘ an ironical taunt used to express contempt ’ , is a challenging nlp problem due to its highly figurative nature | 1 |
we used the penn treebank to perform empirical experiments on the proposed parsing models---we used the penn treebank wsj corpus to perform the empirical evaluation of the considered approaches | 1 |
in this paper , we propose schema induction using coupled tensor factorization ( sictf ) , a novel tensor factorization method for relation schema induction---in this paper , we present sictf , a novel non-negative coupled tensor factorization method for relation schema induction | 1 |
in this study , we propose an effective way to generate and exploit large-scale pseudo training data for zero pronoun resolution task---in this paper , we propose a simple but novel approach to automatically generate large-scale pseudo training data for zero pronoun resolution | 1 |
following zhang and clark , beam search is applied to decoding , and global structured learning is integrated with beam search using earlyupdate---global learning is implemented in the same way as zhang and nivre , using the averaged perceptron algorithm and early update | 1 |
we perform the mert training to tune the optimal feature weights on the development set---we implement the weight tuning component according to the minimum error rate training method | 1 |
for sentence segmentation and tokenization , we rely on the udpipe predicted data files---in summary , we rely for most but not all languages on the tokenization and sentence splitting provided by the udpipe baseline | 1 |
we proposed an unsupervised method for finding lexical variations in roman urdu---and find that the combination of all four feature types is most beneficial for answer reranking | 0 |
we use minimal error rate training to maximize bleu on the complete development data---however , reference resolution remains a challenging problem , partly due to limited speech and language processing capabilities | 0 |
the word vectors used in all approaches are taken from the word2vec google news model---to represent the semantics of the nouns , we use the word2vec method which has proven to produce accurate approximations of word meaning in different nlp tasks | 0 |
tsvetkov et al create synthetic translation options to augment a standard phrase-table---tsvetkov et al create synthetic translation options to augment the phrase-table | 1 |
existing active learning methods usually randomly select a set of unlabeled samples to annotate and then train the initial classifier on them---the recursive application of autoencoders was first introduced in pollack , whose recursive auto-associative memories learn vector representations over pre-specified recursive data structures | 0 |
in this paper , we focus on modeling topics in english datasets using latent dirichlet allocation , a generative model for documents based upon their topics---in this paper we propose an unsupervised method of building bilingual topic hierarchies using topic models | 1 |
these methods detect the words present in a text using different strategies involving lexics , syntax or semantics---these methods detect the words present in a text using different strategies involving lexics , syntax or semantics and then aggregate their values | 1 |
we use stanford corenlp for chinese word segmentation and pos tagging---we use the stanford corenlp for obtaining pos tags and parse trees from our data | 1 |
we measure translation quality via the bleu score---to compare translations , the bleu measure is used | 1 |
we use the stanford ner tool to identify proper names in the source text---we use the stanford pos-tagger and name entity recognizer | 1 |
the first component of our model is a modified reimplementation of the pronoun prediction network introduced by hardmeier et al---the first is a reimplementation of the pronoun prediction neural network proposed by hardmeier et al | 1 |
in recent years a variety of large knowledge bases have been constructed eg , freebase , dbpedia , nell , and yago---over the last few years , several large scale knowledge bases such as freebase , nell , and yago have been developed | 1 |
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