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multiword expressions are word combinations which have idiosyncratic properties relative to their component words , such as taken aback or red tape---multiword expressions are defined as idiosyncratic interpretations that cross word boundaries or spaces

we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit---wu introduced the inversion transduction grammar formalism which treats translation as a process of parallel parsing of the source and target language via a synchronized grammar

an example of such a query is : ¡±asus laptop + opinions¡± , another , more detailed query , might be ¡±asus laptop + positive opinions¡±---asus laptop + opinions ¡± , another , more detailed query , might be ¡± asus laptop + positive opinions ¡±

we evaluated translation quality based on the caseinsensitive automatic evaluation score bleu-4---we used the bleu score to evaluate the translation accuracy with and without the normalization

we show that it outperforms an n-gram model in predicting more than one upcoming word---and demonstrated that our parser outperforms an n-gram model in predicting more than one upcoming word

part-of-speech ( pos ) tagging is the task of assigning each of the words in a given piece of text a contextually suitable grammatical category---part-of-speech ( pos ) tagging is a critical task for natural language processing ( nlp ) applications , providing lexical syntactic information

since text categorization is a task based on predefined categories , we know the categories for classifying documents---text categorization is the classificationof documents with respect to a set of predefined categories

in this paper , we propose a novel and effective approach to sentiment analysis on product reviews---in this paper , we propose a novel hl-sot approach to labeling a product ¡¯ s attributes and their associated sentiments in product reviews

an event schema is a structured representation of an event , it defines a set of atomic predicates or facts and a set of role slots that correspond to the typical entities that participate in the event---event schema is a high-level representation of a bunch of similar events

a combination of sufficient amounts of noise and rich , diverse errors appears to lead to better model performance---on a large corpus of noisy and clean sentences , the model is able to generate rich , diverse errors that better capture the noise

our preliminary experiments show that both methods can improve smt performance without using any additional data---without using any additional resource , both methods can improve smt performance significantly

the net result is a sampler that is non-convergent , overly dependent on its initialisation and can not be said to be sampling from the posterior---hara et al derived turn level ratings from overall ratings of the dialogue which were applied by the users after the interaction on a five point scale within an online questionnaire

our parser plus stochastic disambiguator achieves 79 % f-score under this evaluation regime---as noted earlier , this strategy is characteristic of the systems that participated in the semeval task on classifying semantic relations between nominals , such as butnariu and veale

in systematic experiments , we have demonstrated the strong impact of modeling overall argumentation---we tune the systems using kbest batch mira

the semantic orientation of a phrase is not a mere sum of its component words---twitter is a microblogging site where people express themselves and react to content in real-time

the bleu metric has been used to evaluate the performance of the systems---svms have proven to be an effective means for text categorization as they are capable to robustly deal with high-dimensional , sparse feature spaces

recognition data was again analysed using linear mixed model logistic regression---recognition data was analysed using a linear mixed model logistic regression

peters et al show how deep contextualized word representations model both complex characteristics of word use , and usage across various linguistic contexts---peters et al show that their language model elmo can implicitly disambiguate word meaning with their contexts

in addition , we use an english corpus of roughly 227 million words to build a target-side 5-gram language model with srilm in combination with kenlm---we use srilm to train a 5-gram language model on the target side of our training corpus with modified kneser-ney discounting

we show that emotion-word hashtags often impact emotion intensity , usually conveying a more intense emotion---emotion-word hashtags often impact emotion intensity , often conveying a more intense emotion

in addition , a 5-gram lm with kneser-ney smoothing and interpolation was built using the srilm toolkit---the model was built using the srilm toolkit with backoff and kneser-ney smoothing

distributional semantic models represent lexical meaning in vector spaces by encoding corpora derived word co-occurrences in vectors---a 5-gram language model was built using srilm on the target side of the corresponding training corpus

word2vec , glove and fasttext are the most simple and popular word embedding algorithms---stance detection is the task of automatically determining from the text whether the author of the text is in favor of , against , or neutral towards a proposition or target

the penn discourse treebank , developed by prasad et al , is currently the largest discourse-annotated corpus , consisting of 2159 wall street journal articles---as suggested by the in section 2 , relationals occur closer to the than qualitatives , so this result is consistent

the approach is a direct extension of the incremental algorithm---we base our gre approach on an extension of the incremental algorithm

the lack of da-english parallel corpora suggests pivoting on msa can improve the translation quality---word embedding models are aimed at learning vector representations of word meaning

we use word2vec as the vector representation of the words in tweets---we used word2vec to learn these dense vectors

the dependency parse trees are finally obtained using a phrase structure parser , using the post-processing of the stanford corenlp package---the charniak-lease phrase structure parses are transformed into the collapsed stanford dependency scheme using the stanford tools

efficiency of such learning method may suffer from the mismatch of dialogue state distribution between offline training and online interactive learning stages---as in mitchell et al , a linear regression model was used to learn the mapping from semantic features to brain activity levels

a key feature in our approach is the reliance on a story planner which we acquire automatically by recording events , their participants , and their precedence relationships in a training corpus---to train our model we use markov chain monte carlo sampling

however , li et al have pointed out that the transliteration precision of the phoneme-based approaches could be limited by two main constraints---deep learning techniques have shown enormous success in sequence to sequence mapping tasks

in addition to these two key indicators , we evaluated the translation quality using an automatic measure , namely bleu score---to verify sentence generation quantitatively , we evaluated the sentences automatically using bleu score

we apply sri language modeling toolkit to train a 4-gram language model with kneser-ney smoothing---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit from stolcke

sentence retrieval is to retrieve sentences in response to certain requirements---sentence retrieval is always treated as a special type of document retrieval

1 the atb comprises manually annotated morphological and syntactic analyses of newswire text from different arabic sources , while the ag is simply a huge collection of raw arabic newswire text---in recent years , vector space models ( vsms ) have been proved successful in solving various nlp tasks including named entity recognition , part-of-speech tagging , parsing , semantic role-labeling

we trained a support vector machine for regression with rbf kernel using scikitlearn , which in turn uses libsvm---we applied liblinear via its scikitlearn python interface to train the logistic regression model with l2 regularization

examples of these are freebase , yago , dbpedia , and google knowledge vault---prominent examples include freebase which powers the google knowledge graph , conceptnet , yago , and others

in this paper , we used the decision list to solve the homophone problem---in this paper , we incorporate the written word into the original decision list

different from most work relying on a large number of handcrafted features , collobert and weston proposed a convolutional neural network for srl---collobert and weston deepened the original neural model by adding a convolutional layer and an extra layer for modeling long-distance dependencies

feature weights were set with minimum error rate training on a development set using bleu as the objective function---feature weights are tuned using minimum error rate training on the 455 provided references

heilman et al combined unigram models with grammatical features and trained machine learning models for readability assessment---in recent years has created an increasing need for improvements in organic and sponsored search

open information extraction has been shown to be useful in a number of nlp tasks , such as question answering , relation extraction , and information retrieval---open ie in the monolingual setting has shown to be useful in a wide range of tasks , such as question answering , ontology learning , and summarization

alshawi et al , 2000 ) represents each production in parallel dependency trees as a finite-state transducer---alshawi et al represent each production in parallel dependency tree as a finite transducer

and our experimental results on the ace data set shows the model is effective for coreference resolution---we present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn

we have tested cpra on benchmark data created from freebase---we evaluate cpra on benchmark data created from freebase

the n-gram based language model is developed by employing the irstlm toolkit---the target language model is a 7-gram , binarized irstlm

we use 300 dimensional glove embeddings trained on the common crawl 840b tokens dataset , which remain fixed during training---we used the 300-dimensional glove word embeddings learned from 840 billion tokens in the web crawl data , as general word embeddings

by including predictions of other models as features , we achieve aer of 3.8 on the standard hansards dataset---a set of 500 sentences is used to tune the decoder parameters using the mert

to train our neural algorithm , we apply word embeddings of a look-up from 100-d glove pre-trained on wikipedia and gigaword---in this paper , we propose a forest-based tree sequence to string model , which is designed to integrate the strengths of the forest-based and the tree

importantly , word embeddings have been effectively used for several nlp tasks---word embeddings have been used to help to achieve better performance in several nlp tasks

these methods can not only reduce oov words , but also deal with unknown words---the decoder uses cky-style parsing with cube pruning to integrate the language model

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 well-known social network service that allows users to post short 140 character status update which is called “ tweet ”

the language model was a 5-gram language model estimated on the target side of the parallel corpora by using the modified kneser-ney smoothing implemented in the srilm toolkit---named entity typing is a fundamental building block for many natural-language processing tasks

like soricut and marcu , they formulate the discourse segmentation task as a binary classification problem of deciding whether a word is the boundary or no-boundary of edus---the language model is trained and applied with the srilm toolkit

burkett and klein propose a reranking based method for joint constituent parsing of bitext , which can make use of structural correspondence features in both languages---n-gram translation models helps to address some of the search problems that are nontrivial to handle when decoding

we used the svd implementation provided in the scikit-learn toolkit---for this task , we used the svm implementation provided with the python scikit-learn module

research in this has resulted in the construction of several large scale kgs , such as nell , google knowledge vault and yago---recent research in this area has resulted in the development of several large kgs , such as nell , yago , and freebase , among others

we propose sampling pseudo-negative examples taken from probabilistic language models---we have presented a novel discriminative language model using pseudo-negative examples

the following two subsections review typical methods for each phase---two subsections review typical methods for each phase

we are interested in capturing aspects of coherence as defined by grosz and sidner , based on the attentional state , intentional structure and linguistic structure of discourse---the model weights are automatically tuned using minimum error rate training

in this paper we demonstrate our model by running it on items used in psycholinguistic experiments about human preferences---in this paper we showed how a computational model can mirror human preferences in pronoun resolution and reading times

in our experiments we used rasp , a broad coverage dependency parser , and the opennlp 1 coreference resolution engine---in our experiments this knowledge base was created using the rasp relational parser

sentiment analysis is the natural language processing ( nlp ) task dealing with the detection and classification of sentiments in texts---sentiment analysis is a research area in the field of natural language processing

for language model , we use a trigram language model trained with the srilm toolkit on the english side of the training corpus---in this paper , we propose a framework for automatic evaluation of nlp applications which is able to account for the variation in the human evaluation

we learn our word embeddings by using word2vec 3 on unlabeled review data---we perform pre-training using the skip-gram nn architecture available in the word2vec 13 tool

these experiments demonstrate that fbrnn achieves competitive results compared to the current state-of-the-art---the simplest method of evaluation is direct comparison of the extracted synonyms with a manuallycreated gold standard

all features were log-linearly combined and their weights were optimized by performing minimum error rate training---feature weights were set with minimum error rate training on a development set using bleu as the objective function

the integrated dialect classifier is a maximum entropy model that we train using the liblinear toolkit---we adapted the moses phrase-based decoder to translate word lattices

it is also shown that the analyses provided by the functional uncertainty machinery can be obtained without requiring power beyond mildly context-sensitive grammars---functional uncertainty machinery can be obtained without going beyond the power of mildly context-sensitive grammars

for training our system classifier , we have used scikit-learn---for nb and svm , we used their implementation available in scikit-learn

2 ) our model leverages both the semantic and sentiment correlations between bilingual documents---in the following three aspects , 1 ) we exploit both the semantic and sentiment correlations of the bilingual texts

it has been shown that word embeddings are able to capture to certain semantic and syntactic aspects of words---these embeddings provide a nuanced representation of words that can capture various syntactic and semantic properties of natural language

the language models were interpolated kneser-ney discounted trigram models , all constructed using the srilm toolkit---the language model used was a 5-gram with modified kneserney smoothing , built with srilm toolkit

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 process of producing such a markup

we use the srilm toolkit to compute our language models---the language models in our systems are trained with srilm

capturing these changes is problematic for current language technologies , which are typically developed for speakers of the standard dialect only---language technologies are usually developed for standard dialects , ignoring the linguistic differences in other dialects

we further explore three algorithms in rule matching : 0-1 matching , likelihood matching , and deep similarity matching---we designed and explored three fuzzy rule matching algorithms : 0-1 matching , likelihood matching , and deep similarity matching

as shown in figure 3 , whether or not contributors could be attributed to the hearer did not correlate with the choice of sinceor because---as shown in figure 3 , whether or not contributors could be attributed to the hearer did not correlate with the choice of sinceor

we use the adam optimizer for the gradient-based optimization---for the loss function , we used the mean square error and adam optimizer

the top-down method had better bleu scores for 7 language pairs without relying on supervised syntactic parsers compared to other preordering methods---using the top-down parsing algorithm was faster and gave higher bleu scores than btg-based preordering

we estimate a 5-gram language model using interpolated kneser-ney discounting with srilm---we evaluate the perplexity of the n-gram model with srilm package

the baseline of our approach is a statistical phrase-based system which is trained using moses---the standard phrase-based model that we use as our top-line is the moses system trained over the full europarl v5 parallel corpus

rhetorical structure theory is a framework for describing the organization of a text and what a text conveys by identifying hierarchical structures in text---rhetorical structure theory is one way of introducing the discourse structure of a document to a summarization task

this paper proposes a novel two-stage method for mining opinion words and opinion targets---this paper proposes a two-stage framework for mining opinion words and opinion targets

luong et al learn word representations based on morphemes that are obtained from an external morphological segmentation system---we use the pre-trained word2vec embeddings provided by mikolov et al as model input

for the compilation , we focus on travel blogs , which are defined as travel journals written by bloggers in diary form---ma et al proposed an interactive attention network which interactively learned attentions in the contexts and targets

our system also ranked 4 th out of 40 submissions in identifying the sentiment of sarcastic tweets---and ranking 4 th out of 40 in identifying the sentiment of sarcastic tweets

in this paper , we presented a supervised classification model for keyphrase extraction from scientific research papers that are embedded in citation networks---in many ir and nlp tasks , to our knowledge , we are the first to propose the incorporation of information available in citation networks for keyphrase extraction

charniak and johnson , eg , supply a discriminative reranker that uses eg , features to capture syntactic parallelism across conjuncts---charniak and johnson incorporated some features of syntactic parallelism in coordinate structures into their maxent reranking parser

we map the pos labels in the conll datasets to the universal pos tagset---for the sake of comparability we applied the split to the universal tagset

we used the logistic regression implemented in the scikit-learn library with the default settings---we used svm classifier that implements linearsvc from the scikit-learn library

for this model , we use a binary logistic regression classifier implemented in the lib-linear package , coupled with the ovo scheme---we use the logistic regression implementation of liblinear wrapped by the scikit-learn library

therefore , word segmentation is a crucial first step for many chinese language processing tasks such as syntactic parsing , information retrieval and machine translation---each individual system is a phrase-based system trained using the moses toolkit

ng et al proposed that rather than focusing on just adjective-noun relationships , the subject-verb and verb-object relationships should also be considered for polarity classification---the translation quality is evaluated by bleu and ribes

we propose the joint parsing models by the feed-forward and bi-lstm neural networks---we propose neural network-based joint models for word segmentation , pos tagging and dependency parsing

in this paper , our coreference resolution system for conll-2012 shared task is summarized---we evaluate our approach on the english portion of the conll-2012 dataset

the bleu score is based on the geometric mean of n-gram precision---for the evaluation of the results we use the bleu score

experimental results show that our method can effectively resolve the vocabulary mismatch problem and achieve accurate and robust performance---experimental results show that our system outperforms the base system with a 3 . 4 % gain in f1 , and generates logical forms more accurately

semantic role labeling ( srl ) is a kind of shallow semantic parsing task and its goal is to recognize some related phrases and assign a joint structure ( who did what to whom , when , where , why , how ) to each predicate of a sentence ( cite-p-24-3-4 )---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 )

our randomized lm is based on the bloomier filter---our randomized language model is based on the bloomier filter