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we conducted word alignment bidirectionally with the default parameters and merged them using the grow-diag-final-and heuristic---we conducted word alignment bidirectionally with the default parameters and merged them using the grow-diag-final-and heuristics | 1 |
this parsing approach is very similar to the one used successfully by nivre et al , but we use a maximum entropy classifier to determine parser actions , which makes parsing considerably faster---in this paper , we discuss the benefits of tightly coupling speech recognition and search components | 0 |
the target language model was a standard ngram language model trained by the sri language modeling toolkit---a trigram language model with modified kneser-ney discounting and interpolation was used as produced by the srilm toolkit | 1 |
in this paper , we also assume that terms in a taxonomy are given and concentrate on the subtask of relation formation---in this paper , we take an incremental clustering approach , in which terms and relations are added into a taxonomy | 1 |
this work describes an automated quality-monitoring system that addresses these problems---this paper describes an automated system for assigning quality | 1 |
we use the datasets , experimental setup , and scoring program from the conll 2011 shared task , based on the ontonotes corpus---throughout this work , we use the datasets from the conll 2011 shared task 2 , which is derived from the ontonotes corpus | 1 |
the precisions and bleu-4 scores of the baseline system and our approach are shown in table 4---we use pre-trained glove embeddings to represent the words | 0 |
we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit from stolcke | 1 |
zens and ney use a disk-based prefix tree , enabling efficient access to phrase tables much too large to fit in main memory---zens and ney remove constraints imposed by the size of main memory by using an external data structure | 1 |
we presented a novel two-stage technique for detecting speech disfluencies based on ilp---we present a novel two-stage technique for detecting speech disfluencies based on integer | 1 |
recent studies show that character sequence labeling is an effective formulation of chinese word segmentation---recent studies show that character sequence labeling is an effective method of chinese word segmentation for machine learning | 1 |
each sentence in the dataset is parsed using stanford dependency parser---sentences are tagged and parsed using the stanford dependency parser | 1 |
plagiarism is a major issue in science and education---plagiarism is a very significant problem nowadays , specifically in higher education institutions | 1 |
berant et al present a semantic parser that does not need to be trained through the annotated logical form---berant et al proposed a semantic parsing model that can be trained from qna pairs , which are much easier to obtain than correct kb queries used previously | 1 |
thus , in this work , we try to explore a path to use the target domain specific information with as few as possible target labeled data---in this work , we propose a method to simultaneously extract domain specific and invariant representations | 1 |
the clustering method used in this work is latent dirichlet allocation topic modelling---taglda is a representative latent topic model by extending latent dirichlet allocation | 1 |
we also used word2vec to generate dense word vectors for all word types in our learning corpus---we then used word2vec to train word embeddings with 512 dimensions on each of the prepared corpora | 1 |
li and yarowsky proposed an unsupervised method extracting the relation between a full-form phrase and its abbreviation from monolingual corpora---li and yarowsky propose an unsupervised method to extract the relations between full-form phrases and their abbreviations | 1 |
semantic role labeling ( srl ) is the task of identifying the arguments of lexical predicates in a sentence and labeling them with semantic roles ( cite-p-13-3-3 , cite-p-13-3-11 )---semantic role labeling ( srl ) is defined as the task to recognize arguments for a given predicate and assign semantic role labels to them | 1 |
we use the nltk library to compute the pathlen similarity and lin similarity measures---word alignment is a fundamental problem in statistical machine translation | 0 |
psl is a probabilistic logic framework designed to have efficient inference---1 psl is a probabilistic programming system that allows models to be specified using a declarative , rule-like language | 1 |
in particular , we address document summarization in the framework of multitask learning using curriculum learning for sentence extraction and document classification---summaries , we demonstrated document summarization in the framework of multi-task learning with curriculum learning for sentence extraction and document classification | 1 |
in the experiments we trained 5-gram language models on the monolingual parts of the bilingual corpora using srilm---for the document embedding , we use a doc2vec implementation that downsamples higher-frequency words for the composition | 0 |
the translation results are evaluated by caseinsensitive bleu-4 metric---translation performances are measured with case-insensitive bleu4 score | 1 |
finally , the graph is clustered using chinese whispers---we further add skip connections between the lstm layers to the softmax layers , since they are proved effective for training neural networks | 0 |
mikolov et al , 2013a ) proposes skip-gram and continuous bag-of-words models based on a single-layer network architecture---word sense disambiguation ( wsd ) is a key task in computational lexical semantics , inasmuch as it addresses the lexical ambiguity of text by making explicit the meaning of words occurring in a given context ( cite-p-18-3-10 ) | 0 |
we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model---we created 5-gram language models for every domain using srilm with improved kneserney smoothing on the target side of the training parallel corpora | 1 |
table 2 shows results for the strategies 1 , 2 and 3 in terms of bleu---table 2 gives the results measured by caseinsensitive bleu-4 | 1 |
this enables the low-resource language to utilize the lexical and sentence representations of the higher resource languages---which enables low-resource languages to utilize the sentence representation of the higher resource languages | 1 |
relation extraction is the problem of populating a target relation ( representing an entity-level relationship or attribute ) with facts extracted from natural-language text---for language model scoring , we use the srilm toolkit training a 5-gram language model for english | 0 |
we evaluated the translation quality of the system using the bleu metric---we evaluated the translation quality using the bleu-4 metric | 1 |
metamap 5 is a tool capable of detecting mentions of concepts from the extensive umls metathesaurus in text---given a bilingual website , the mining systems use predefined url patterns to discover candidate parallel documents within the site | 0 |
in this paper , we develop an approach based on recurrent neural networks to learn the relation between an essay and its assigned score , without any feature engineering---in this paper , we report a system based on neural networks to take advantage of their modeling capacity and generalization power for the automated essay | 1 |
however , efficient access to this new resource has been limited by the immense size of the data---but access is severely impeded by the lack of efficient tools for managing the huge amount of provided data | 1 |
we use a random forest classifier , as implemented in scikit-learn---by our method , we believe that ontologizing lexical-semantic resources will be feasible | 0 |
our machine translation system is a phrase-based system using the moses toolkit---our mt decoder is a proprietary engine similar to moses | 1 |
experiments show that these methods are very effective for topical keyphrase extraction---proposed methods are very effective in topical keyphrase extraction | 1 |
kim et al adopt a walk-weighted subsequence kernel based on shortest dependency paths to explore various substructures such as e-walks , partial match , and non-contiguous paths---kim et al adopt walk-weighted subsequence kernel based on dependency paths to explore various substructures such as e-walks , partial match , and non-contiguous paths | 1 |
to evaluate more efficiently k n , we use the recursive formulation proposed in based on a dynamic programming implementation---to evaluate k n more efficiently , we use the recursive formulation based on a dynamic programming implementation | 1 |
in this paper , we attempt to capture some of this implicit context by exploiting the social network structure in microblogs---mention , our approach is able to exploit the social network as a source of contextual information | 1 |
we found farasa by orders of magnitude faster than both---we observed farasa to be at least an order of magnitude faster than both | 1 |
the learning algorithm used is a variation of the winnow update rule incorporated in snow , a multi-class classifier that is specifically tailored for large scale learning tasks---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit from stolcke | 0 |
relation extraction is a core task in information extraction and natural language understanding---relation extraction ( re ) is the task of extracting semantic relationships between entities in text | 1 |
our clustering algorithm was applied to an ltag grammar automatically extracted from sections 02-21 of the penn treebank ,---senseclusters is a freely available system that identifies similar contexts in text | 0 |
moses was used as a baseline system for comparison of the nmt model---the smt systems were built using the moses toolkit | 1 |
previous work showed that word clusters derived from an unlabelled dataset can improve the performance of many nlp applications---semi-supervised word cluster features have been successfully applied to many nlp tasks | 1 |
to minimize the objective , we use stochastic gradient descent with the diagonal variant of adagrad---the language model was constructed using the srilm toolkit with interpolated kneser-ney discounting | 0 |
the srilm toolkit was used for training the language models using kneser-ney smoothing---the translation quality is evaluated by case-insensitive bleu and ter metrics using multeval | 0 |
we use mini-batch update and adagrad to optimize the parameter learning---coreference resolution is the next step on the way towards discourse understanding | 0 |
we used the svm implementation provided within scikit-learn---we implemented the different aes models using scikit-learn | 1 |
the 4-gram language model was trained with the kenlm toolkit on the english side of the training data and the english wikipedia articles---the smt systems used a kenlm 5-gram language model , trained on the mono-lingual data from wmt 2015 | 1 |
we implemented this model using the srilm toolkit with the modified kneser-ney discounting and interpolation options---we used the srilm toolkit to build unpruned 5-gram models using interpolated modified kneser-ney smoothing | 1 |
here , independent binary sub-classifiers detect the different decision dialogue acts , and then based on the sub-classifier hypotheses , a super-classifier determines which dialogue regions are decision discussions---we also report an evaluation on all thirteen languages of the conll-x shared task , for comparison with the results by nivre and mcdonald | 0 |
extensions for transition systems have been proposed to handle non-projective structures with additional actions---systems have been proposed for projective dependency trees , non-projective , or even unknown classes | 1 |
first , we show that , due to its computational complexity , it is difficult to straightforwardly apply previously studied techniques of bilingual term correspondence estimation from comparable corpora , especially in the case of large scale evaluation such as those presented in this paper---that , due to its computational complexity , it is difficult to straightforwardly apply previously studied techniques of bilingual term correspondence estimation from comparable corpora , especially in the case of large scale evaluation such as those presented in this paper | 1 |
semantic parsing is the task of translating text to a formal meaning representation such as logical forms or structured queries---semantic parsing is the problem of mapping natural language strings into meaning representations | 1 |
knowitall applies the hyponym patterns to extract instances from the web and ranks them by relevance using mutual information---the knowitall system also uses hyponym patterns to extract class instances from the web and evaluates them further by computing mutual information scores based on web queries | 1 |
we adapted the moses phrase-based decoder to translate word lattices---this paper presents a general-purpose realizer based on log-linear models for directly linearizing dependency relations | 0 |
fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization---opinion analysis involves extraction of opinion targets ( or aspect terms ) and opinion expressions ( or opinion terms ) from each review sentence | 1 |
we ran experiments on the wall street journal portion of the english penn treebank data set , using a standard data split---recent years have witnessed burgeoning development of statistical machine translation research , notably phrase-based and syntax-based approaches | 0 |
in our implementation , we use the shrinkage method suggested by schapire and singer and collins and koo---at present , we use a feature set that is similar to the one used by collins and koo | 1 |
using espac medlineplus , we trained an initial phrase-based moses system---we used the moses toolkit to train the phrase tables and lexicalized reordering models | 1 |
we use the dictionary of affect in language , augmented with wordnet for coverage---we used a caseless parsing model of the stanford parser for a dependency representation of the messages | 0 |
we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit---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 | 1 |
grammar induction is the task of learning a grammar from a set of unannotated sentences---grammar induction is a central problem in computational linguistics , the aim of which is to induce linguistic structures from an unannotated text corpus | 1 |
weller et al propose using noun class information to model selectional preferences of prepositions in a string-to-tree translation system---weller et al use noun class information as tree labels in syntactic smt to model selectional preferences of prepositions | 1 |
we use liblinear logistic regression module to classify document-level embeddings---for the sentiment polarity slot , we used a a supervised machine learning classifier , having bag-of-words ( bow ) , lemmas , bigrams after | 0 |
we extract the corresponding feature from the output of the stanford parser---we use the stanford parser to extract a set of dependencies from each comment | 1 |
they exist in many types of text and cause major problems in all kinds of natural language processing applications---consequently , they pose problems to most natural language processing applications | 1 |
reisinger and mooney and huang et al also presented methods that learn multiple embeddings per word by clustering the contexts---huang et al presented an rnn model that uses document-level context information to construct more accurate word representations | 1 |
one of the first challenges in sentiment analysis is the vast lexical diversity of subjective language---sentiment analysis is a nlp task that deals with extraction of opinion from a piece of text on a topic | 1 |
faruqui et al apply post-processing steps to existing word embeddings in order to bring them more in accordance with semantic lexicons such as ppdb and framenet---faruqui et al employ semantic relations of ppdb , wordnet , framenet to retrofit word embeddings for various prediction tasks | 1 |
experiments on chinese-english parallel propbank show that our joint inference model is very effective for bilingual srl---experiments on chinese-english parallel propbank shows that our model significantly outperforms monolingual srl combination systems | 1 |
reichart and rappoport , 2007 ) are the first to report successful self-training using a generative parsing model only---reichart and rappoport show that the number of unknown words is a good indicator of the usefulness of self-training when applied to small seed data sets | 1 |
we extracted them from the recent valex lexicon which provides scf frequency information for 6,397 english verbs---for phrase-based smt translation , we used the moses decoder and its support training scripts | 0 |
hence if possible it is always better to integrate lms directly into the decoder---we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus | 0 |
in this paper , we focus on the class of methods which induce a shared feature representation---to capture the relation between words , kalchbrenner et al propose a novel cnn model with a dynamic k-max pooling | 0 |
we find that entice is able to significantly increase nell¡¯s knowledge density by a factor of 7.7 at 75.5 % accuracy---by using entice , we are able to increase nell ¡¯ s knowledge density by a factor of 7 . 7 | 1 |
twitter is a subject of interest among researchers in behavioral studies investigating how people react to different events , topics , etc. , as well as among users hoping to forge stronger and more meaningful connections with their audience through social media---twitter is the medium where people post real time messages to discuss on the different topics , and express their sentiments | 1 |
we use the stanford pos tagger to obtain the perspectives p and l---after splitting snippets into sentences , we applied named entity recognizer to recognize entities in sentences | 0 |
these language models were built up to an order of 5 with kneser-ney smoothing using the srilm toolkit---a trigram language model with modified kneser-ney discounting and interpolation was used as produced by the srilm toolkit | 1 |
discourse parsing is the task of identifying the presence and the type of the discourse relations between discourse units---word sense disambiguation ( wsd ) is a difficult natural language processing task which requires that for every content word ( noun , adjective , verb or adverb ) the appropriate meaning is automatically selected from the available sense inventory 1 | 0 |
we trained a 4-gram language model on the xinhua portion of gigaword corpus using the sri language modeling toolkit with modified kneser-ney smoothing---stance detection is the task of automatically determining whether the authors of a text are against or in favour of a given target | 0 |
alternatively , in a low resource machine translation ( mt ) setting , it is reasonable to assume a small amount of parallel data from which a bilingual dictionary can be extracted for supervision---even in a low resource machine translation setting , where induced translations have the potential to improve performance substantially , it is reasonable to assume access to some amount of data | 1 |
we initialize these word embeddings with glove vectors---we use 300-dimensional word embeddings from glove to initialize the model | 1 |
in this work , we use the expectation-maximization algorithm---model fitting for our model is based on the expectation-maximization algorithm | 1 |
huang and zweig presented a maximum entropy-based tagging approach to punctuation insertion in spontaneous english conversational speech , where both lexical and prosodic features were exploited---huang and zweig attempted to explore pos features and prosodic features for inserting punctuations in automatically recognized speech texts using mems | 1 |
we also demonstrate that extracted translations significantly improve the performance of the moses machine translation system---adopting the extracted translations can significantly improve the performance of the moses machine translation system | 1 |
we show that reducing the level of anisomorphism yields consistent gains in cross-lingual transfer tasks---our anisomorphism reduction procedure can assist model transfer in cross-lingual tasks | 1 |
miwa and bansal adopted a bidirectional tree lstm model to jointly extract named entities and relations under a dependency tree structure---later , miwa and bansal have implemented an end-to-end neural network to construct a context representation for joint entity and relation extraction | 1 |
we train a linear support vector machine classifier using the efficient liblinear package---translation retrieval is firstly introduced in translation memory systems | 0 |
we tune the feature weights with batch k-best mira to maximize bleu on a development set---tang et al 2002 ) use the density information to weight the selected examples but do not use it to select a sample | 0 |
the expectation maximization algorithm is a general framework for estimating the parameters of a probability model when the data has missing values---the em algorithm is a method to estimate a model that has the maximal likelihood of the data when some variables can not be observed | 1 |
as the word embeddings , we used the 300 dimension vectors pre-trained by glove 6---for all experiments , we used a vocabulary of the first 100,000 word vectors in glove 7 | 1 |
in this paper , we present a method which linearizes amr graphs in a way that captures the interaction of concepts and relations---in this paper , we describe a sequenceto-sequence model for amr parsing and present different ways to tackle the data | 1 |
twitter is a widely used microblogging platform , where users post and interact with messages , “ tweets ”---yang and kirchhoff use phrase-based backoff models to translate words that are unknown to the decoder , by morphologically decomposing the unknown source word | 0 |
several studies have used social network analysis or email traffic patterns for extracting social relations from online communication---there have been several studies using social network analysis for extracting social relations from emails | 1 |
srilm was employed to train a 5-gram language models with all japanese corpus in cj corpus and ej corpus---a 4-gram language model is trained on the monolingual data by srilm toolkit | 1 |
lda is a generative probabilistic model where documents are viewed as mixtures over underlying topics , and each topic is a distribution over words---we present a learning method for word embeddings specifically designed to be useful for relation classification | 0 |
transliteration is a process of rewriting a word from a source language to a target language in a different writing system using the word ’ s phonological equivalent---transliteration is a subtask in ne translation , which translates nes based on the phonetic similarity | 1 |
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