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we initialize the word embedding matrix with pre-trained glove embeddings---we use the pre-trained glove vectors to initialize word embeddings | 1 |
we use the stanford parser for english language data---for english , we use the stanford parser for both pos tagging and cfg parsing | 1 |
in particular , we integrated character language model that and proposed , with our system---besides , we used the character language model that and proposed , on the vlsp dataset and our vtner dataset | 1 |
however , such annotation is impractical and time-consuming for large corpora---however , this is cumbersome and time-consuming for large corpora | 1 |
mann and yarowsky proposed a bottom-up agglomerative clustering algorithm based on extracting local biographical information as features---mann and yarowsky used semantic information extracted from documents referring to the target person in an hierarchical agglomerative clustering algorithm | 1 |
multiword expressions are combinations of words which are lexically , syntactically , semantically or statistically idiosyncratic---multiword expressions are lexical items that can be decomposed into single words and display idiosyncratic features | 1 |
we used the svd implementation provided in the scikit-learn toolkit---within this subpart of our ensemble model , we used a svm model from the scikit-learn library | 1 |
the stochastic gradient descent with back-propagation is performed using adadelta update rule---we apply the stochastic gradient descent algorithm with mini-batches and the adadelta update rule | 1 |
sentiment lexicon is a set of words ( or phrases ) each of which is assigned with a sentiment polarity score---a sentiment lexicon is a list of words and phrases , such as “ excellent ” , “ awful ” and “ not bad ” , each of them is assigned with a positive or negative score reflecting its sentiment polarity and strength ( cite-p-18-3-8 ) | 1 |
all n-grams of both texts are then compared using the containment measure---stopword n-grams of both texts are compared using the containment measure | 1 |
mikolov et al proposed an efficient method to learn word vectors through feedforward neural networks by eliminating the hidden layer---the state-of-the-art unsupervised berkeley aligner 3 lexicalized reordering gives better performance than simple distance-based reordering | 0 |
knowledge bases such as freebase and yago play a pivotal role in many nlp related applications---various large-scale knowledge bases such as freebase , dbpedia , and yagoare widely used in many nlp tasks | 1 |
we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit---in this paper , we explore the potential of quantum theory as a formal framework for capturing lexical meaning | 0 |
our model is inspired by the network architectures used in for performing various sentence classification tasks---finally , we perform an extrinsic evaluation of the different embeddings | 0 |
in this paper , we propose a neural semi-supervised model for japanese pas analysis---we use the word2vec skip-gram model to learn initial word representations on wikipedia | 0 |
finkel and manning apply this method to dependency parsing , by using a hierarchical bayesian model---finkel and manning demonstrate the hierarchical bayesian extension of this where domain-specific models draw from a general base distribution | 1 |
to mitigate overfitting , we apply the dropout method to the inputs and outputs of the network---to reduce overfitting , we apply the dropout method to regularize our model | 1 |
we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model---we trained a 4-gram language model on this data with kneser-ney discounting using srilm | 1 |
we extract our paraphrase grammar from the french-english portion of the europarl corpus---we use the europarl english-french parallel corpus plus around 1m segments of symantec translation memory | 1 |
existing approaches to this task require substantial human effort---however , this approach still requires substantial human effort | 1 |
our experimental results show that our proposed approaches significantly outperform existing strong baselines ( e.g . dnorm ) across all of the three datasets---our experimental results show that our proposed approaches significantly and consistently outperform existing effective baselines , which achieved state-of-the-art performance | 1 |
semantic parsing is the task of translating text to a formal meaning representation such as logical forms or structured queries---we use the sentiment pipeline of stanford corenlp to obtain this feature | 0 |
we apply the algorithm on the conll-2008 shared task data , and obtain the same evaluation score as the best previously published system that simultaneously learns syntactic and semantic structure ( cite-p-11-3-24 )---an argument usually consists of a central claim ( or conclusion ) and several supporting premises | 0 |
these experiments demonstrate that fbrnn achieves competitive results compared to the current state-of-the-art---experimental results demonstrate that fbrnn is competitive with the state-of-the-art methods | 1 |
sentiment classification is the task of identifying the sentiment polarity ( e.g. , positive or negative ) of * 1 corresponding author a natural language text towards a given topic ( cite-p-18-1-19 , cite-p-18-3-1 ) and has become the core component of many important applications in opinion analysis ( cite-p-18-1-2 , cite-p-18-1-10 , cite-p-18-1-15 , cite-p-18-3-4 )---we use pre-trained word2vec word vectors and vector representations by tilk et al to obtain word-level similarity information | 0 |
inversion transduction grammar is an adaptation of cfg to bilingual parsing---inversion transduction grammar is an adaptation of scfg to bilingual parsing | 1 |
semantic parsing is the task of transducing natural language ( nl ) utterances into formal meaning representations ( mrs ) , commonly represented as tree structures---semantic parsing is the task of mapping natural language utterances to machine interpretable meaning representations | 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 )---shi et al constructs a more unified framework , ste to address the problem of polysemy | 0 |
we use the opensource moses toolkit to build a phrase-based smt system---we used moses , a phrase-based smt toolkit , for training the translation model | 1 |
chen et al introduced a lexical syntactic feature architecture to detect offensive content and identify potential offensive users in social media---chen et al , 2012 ) used lexical and parser features , for detecting comments from youtube that are offensive | 1 |
this paper presents a novel word reordering model that employs a shift-reduce parser for inversion transduction grammars---this paper presents a parser-based word reordering model that employs a shift-reduce parser for inversion transduction grammars | 1 |
we use srilm toolkits to train two 4-gram language models on the filtered english blog authorship corpus and the xinhua portion of gigaword corpus , respectively---to calculate language model features , we train traditional n-gram language models with ngram lengths of four and five using the srilm toolkit | 1 |
we compute the syntactic features only for pairs of event mentions from the same sentence , using the stanford dependency parser---the most important systematic interactions among variables limits the number of parameters to be estimated , supports computational efficiency , and provides an understanding of the data | 0 |
in this paper , we present a new multilingual algorithm for dependency parsing---we present a novel algorithm for multilingual dependency parsing | 1 |
to solve the problem , this paper proposes an incremental parsing method based on an adjoining operation---this paper has proposed an incremental parser based on an adjoining operation | 1 |
these observations motivate us to utilize aspect ranking results to assist classifying the sentiment of review documents---we apply aspect ranking results to the application of document-level sentiment classification , and improve the performance significantly | 1 |
several classifications of verbs have also been proposed based on various types of verb alternation and syntactic case patterns---we introduce a simple method to translate between multiple languages using a single model , taking advantage of multilingual data to improve nmt | 0 |
we evaluate system output automatically , using the bleu-4 modified precision score with the human written sentences as reference---we used a phrase-based smt model as implemented in the moses toolkit | 0 |
the weights of these features are then learned using a discriminative training algorithm---the weights for the loglinear model are learned using the mert system | 1 |
lin and he proposed a joint sentimenttopic model for unsupervised joint sentiment topic detection---lin and he propose a joint topic-sentiment model , but topic words and sentiment words are still not explicitly separated | 1 |
the next level consists of surface speech-acts , which are abstractions of the actions of uttering particular sentences with particular syntactic structures---the next level consists of concept-activation actions , which entail the planning of descriptions that are mutually believed by the speaker and hearer to refer to objects in the world | 1 |
as cite-p-16-8-17 points out , input length does not cause a noticeable increase in running time up to 35 to 40 input tokens---as cite-p-16-8-17 points out , input length does not cause a noticeable increase in running time | 1 |
the presented approach requires a restriction on the entity-tuple embedding space---for nb and svm , we used their implementation available in scikit-learn | 0 |
concretely , we split each review into sentences with a pre-trained tokenizer for english from nltk---in particular , we used the wordnet lemmatizer in nltk to lemmatize the verbs in the corpus | 1 |
this paper proposed a novel method for learning probability models of subcategorization preference of verbs---discourse parsing is a challenging task and plays a critical role in discourse analysis | 0 |
this type of features are based on a trigram model with kneser-ney smoothing---the language model is a 5-gram lm with modified kneser-ney smoothing | 1 |
we use pre-trained word vectors of glove for twitter as our word embedding---we used the 200-dimensional word vectors for twitter produced by glove | 1 |
we evaluated our approaches using the englishfrench hansards data from the 2003 naacl shared task---we applied our algorithms to word-level alignment using the english-french hansards data from the 2003 naacl shared task | 1 |
for all the experiments below , we utilize the pretrained word embeddings word2vec from mikolov et al to initialize the word embedding table---we use sri language modeling toolkit to train a 5-gram language model on the english sentences of fbis corpus | 0 |
popular topic modeling techniques include latent dirichlet allocation and probabilistic latent semantic analysis---probabilistic topic modeling has attracted significant attention with techniques such as probabilistic latent semantic analysis and lda | 1 |
all feature models are estimated in the in-domain corpus with standard techniques---the probabilistic language model is constructed on google web 1t 5-gram corpus by using the srilm toolkit | 1 |
in this paper , we examined and evaluated the applicability of bagging and boosting techniques to coreference resolution---in this paper , we present a potential approach for improving the performance of coreference resolution | 1 |
in this paper , we study the problem of manually correcting automatic annotations of natural language in as efficient a manner as possible---the baseline system is a pbsmt engine built using moses with the default configuration | 0 |
next we consider the context-predicting vectors available as part of the word2vec 6 project---we assume that a morphological analysis consists of three processes : tokenization , dictionary lookup , and disambiguation | 0 |
han and lavie and han et al use their own formalism in conjunction with reasoning using temporal constraint propagation---han and lavie use their own formalism in conjunction with reasoning using temporal constraint propagation | 1 |
each lemma is analyzed by the morphological analyzer d茅rif , adapted to the treatment of medical words---each lemma was analyzed by the morphological analyzer d茅rif , adapted to the treatment of medical words | 1 |
to determine the word classes , one can use the algorithm of brown et al , which finds the classes that give high mutual information between the classes of adjacent words---in order to cluster lexical items , we use the algorithm proposed by brown et al , as implemented in the srilm toolkit | 1 |
we use several classifiers including logistic regression , random forest and adaboost implemented in scikit-learn---for training the model , we use the linear kernel svm implemented in the scikit-learn toolkit | 1 |
we first obtain word representations using the popular skip-gram model with negative sampling introduced by mikolov et al and implemented in the gensim package---gram language models are trained over the target-side of the training data , using srilm with modified kneser-ney discounting | 0 |
this was the best performing method on average in the 2016 semeval task 6 shared task---socher et al , 2012 , uses a recursive neural network in relation extraction , and further use lstm | 0 |
closely related work using methods that analyze sentiment on a deep level is done by polanyi and zaenen , who consider the role of lexical and discourse context of the attitudinal sentences---finally , a linear model is trained using a variation of the averaged perceptron algorithm | 0 |
the twin components are trained and used simultaneously in our coreference system---components are intimately trained and used simultaneously in our coreference system | 1 |
our model is thus a form of quasi-synchronous grammar---in modeling p , we make use of quasi-synchronous grammar | 1 |
gram language models are trained over the target-side of the training data , using srilm with modified kneser-ney discounting---punyakanok et al used a generalized tree-edit distance method to score mappings between dependency parse trees | 0 |
the idea of inducing selectional preferences from corpora was introduced by resnik---one of the first to automatically induce selectional preferences from corpora was resnik | 1 |
targeted models are used for within-kb reasoning ; they rely on the closed-world assumption and often do not scale with the number of relations---models are not suited for in-kb reasoning ; an individual pair of entities usually does not occur in more than one kb relation | 1 |
collobert et al developed a general neural network architecture for sequence labeling tasks---collobert et al and zhou and xu worked on the english constituent-based srl task using neural networks | 1 |
hence we use the expectation maximization algorithm for parameter learning---thus , optimizing this objective remains straightforward with the expectation-maximization algorithm | 1 |
collobert and weston presented a much deeper model consisting of several layers for feature extraction , with the objective of building a general architecture for nlp tasks---collobert and weston used convolutional neural networks in a multitask setting , where their model is trained jointly for multiple nlp tasks with shared weights | 1 |
in this paper we introduce the task of detecting content-heavy sentences in cross-lingual context---in this paper , we systematically study the relationship between the presence of full-stop commas in the sentence and whether it is content-heavy | 1 |
turkish is a free-constituent order language with complex agglutinative inflectional and derivational morphology and presents interesting challenges for statistical parsing , as in general , dependency relations are between “ portions ” of words – called inflectional groups---turkish is a morphologically complex language with very productive inflectional and derivational processes | 1 |
we used a phrase-based smt model as implemented in the moses toolkit---we use the moses software to train a pbmt model | 1 |
the first contribution in this paper is that a novel language model , the binarized embedding language model ( belm ) is proposed to reduce the memory consumption---in this paper , a novel language model , the binarized embedding language model ( belm ) is proposed to solve the problem | 1 |
more recently , bansal and klein proposed features for both dependency and constituency parsing based on web counts from the google n-grams corpus---most recently , bansal and klein improved the berkeley parser by using surface counts from google n-grams | 1 |
word embeddings have also been effectively employed in several tasks such as named entity recognition , adjectival scales and text classification---methods can improve the retrieval performance , and their performance is comparable with the best systems of the trec medical records tracks | 0 |
for training the translation model and for decoding we used the moses toolkit---we trained the statistical phrase-based systems using the moses toolkit with mert tuning | 1 |
the knowledge graph semantics are integrated as distributed representations of entities---the experiments were performed on english-togerman translation using a standard phrase-based smt system , trained using the moses toolkit , with a 5-gram language model | 0 |
topic coreference resolution resembles another well-known problem in nlp -noun phrase coreference resolution that considers machine learning frameworks---dependency parsing is a very important nlp task and has wide usage in different tasks such as question answering , semantic parsing , information extraction and machine translation | 0 |
for both languages , we used the srilm toolkit to train a 5-gram language model using all monolingual data provided---shallow semantic representations could prevent the sparseness of deep structural approaches and the weakness of bow models | 0 |
our algorithm filters incorrect inference rules and identifies the directionality of the correct ones---we propose an algorithm called ledir that filters incorrect inference rules and identifies the directionality of correct ones | 1 |
adding more complex features may not improve the performance much or may even hurt the performance---adding more complex features may not improve the performance much , and may even hurt the performance | 1 |
framenet ( cite-p-22-1-8 ) is a rich linguistic resource containing considerable information about lexical and predicate-argument semantics in english---we use pre-trained vectors from glove for word-level embeddings | 0 |
we work with the phrase-based smt framework as the baseline system---we use a standard phrasebased translation system | 1 |
we follow the approach of chambers and jurafsky , evaluating our models for predicting script events in a narrative cloze task---we follow previous work in using the narrative cloze task to evaluate statistical scripts | 1 |
relation extraction is a traditional information extraction task which aims at detecting and classifying semantic relations between entities in text ( cite-p-10-1-18 )---relation extraction ( re ) is the task of identifying instances of relations , such as nationality ( person , country ) or place of birth ( person , location ) , in passages of natural text | 1 |
in this paper , we present a comprehensive study of the relationship between an individual ’ s personal traits and his/her brand preferences---in previous research , in this study , we want to systematically investigate the relationship between a comprehensive set of personal traits and brand preferences | 1 |
the data used is the standard conll 2008 shared task version of penn treebank wsj and propbank---the conll data set was taken from the wall street journal portion of the penn treebank and converted into a dependency format | 1 |
question answering ( qa ) is a challenging task that draws upon many aspects of nlp---our empirical results demonstrate that both enhancements lead to about a 9 % absolute performance gain | 0 |
in this setting , reuse may be mixed with text derived from other sources---in this case , it may be preferable to look for near-duplicate documents | 1 |
the birnn is implemented with lstms for better long-term dependencies handling---such frameworks include recursive auto-encoders , denoising autoencoders , and others | 0 |
we used 200 dimensional glove word representations , which were pre-trained on 6 billion tweets---we used the 300-dimensional glove word embeddings learned from 840 billion tokens in the web crawl data , as general word embeddings | 1 |
the log-linear feature weights are tuned with minimum error rate training on bleu---the weights 位 m in the log-linear model were trained using minimum error rate training with the news 2009 development set | 1 |
in particular , we use the neural-network based models from , also referred as word embeddings---in addition , we can use pre-trained neural word embeddings on large scale corpus for neural network initialization | 1 |
for the intrinsic evaluation , we build a benchmark consisting of over 115,000 word analogy questions for the arabic language---we first build a benchmark for the arabic language that can be utilized to perform intrinsic evaluation of different word | 1 |
we used moses to train an alignment model on the created paraphrase dataset---we trained the statistical phrase-based systems using the moses toolkit with mert tuning | 1 |
we implement a distributed training strategy for the perceptron algorithm using the mapreduce framework---we use the idea of iterative parameter mixture to parallelize the training process | 1 |
the evaluation metric is the case-insensitive bleu4---case-insensitive bleu4 was used as the evaluation metric | 1 |
1 email is a written medium of asynchronous multi-party communication---thus , email is a distinct linguistic genre that poses its own challenges to summarization | 1 |
we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit---in the translation tasks , we used the moses phrase-based smt systems | 0 |
sentences are passed through the stanford dependency parser to identify the dependency relations---we apply the rules to each sentence with its dependency tree structure acquired from the stanford parser | 1 |
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