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coreference resolution is the task of partitioning a set of mentions ( i.e . person , organization and location ) into entities---zhang and kordoni and cholakov et al , on the other hand , have trained a maximum entropy classifier with features extracted from the grammar in order to acquire new lexical entries for the erg and the gg , respectively | 0 |
we use the moses mt framework to build a standard statistical phrase-based mt model using our old-domain training data---as a baseline system for our experiments we use the syntax-based component of the moses toolkit | 1 |
the twin components are trained and used simultaneously in our coreference system---to build the local language models , we use the srilm toolkit , which is commonly applied in speech recognition and statistical machine translation | 0 |
multi-task joint modeling has been shown to effectively improve individual tasks---multi-task learning using a related auxiliary task can lead to stronger generalization and better regularized models | 1 |
based on hypothesis 1 , we learn sense-based embeddings from a large data set , using the continuous skip-gram model---to start with , we replace word types with corresponding neural language model representations estimated using the skip-gram model | 1 |
recently , nmt has become a quite popular and effective alternative to traditional phrase-based statistical machine translation---in recent years , phrase-based systems for statistical machine translation have delivered state-of-the-art performance on standard translation tasks | 1 |
miwa and bansal were among the first to use neural networks for end-to-end relation extraction , showing highly promising results---later , miwa and bansal have implemented an end-to-end neural network to construct a context representation for joint entity and relation extraction | 1 |
socher et al introduced a deep learning framework called semi-supervised recursive autoencoders for predicting sentencelevel sentiment distributions---socher et al and socher et al present a framework based on recursive neural networks that learns vector space representations for multi-word phrases and sentences | 1 |
by using the wasserstein distance between distributions , the wordto-word semantic relationship is taken into account in a principled way---wasserstein distance takes into account the cross-term relationship between different words in a principled fashion | 1 |
collobert et al showed that a neural model could achieve close to state-of-the-art results in part of speech tagging and chunking by relying almost only on word embeddings learned with a language model---collobert et al propose a multi-task learning framework with dnn for various nlp tasks , including part-of-speech tagging , chunking , named entity recognition , and semantic role labelling | 1 |
tai et al put forward the tree-structured long short-term memory networks to improve the semantic representations---because shorter sentences are generally better processed by nlp systems , it could be used as a preprocessing step which facilitates and improves the performance of parsers | 0 |
our 5-gram language model was trained by srilm toolkit---we trained a 5-grams language model by the srilm toolkit | 1 |
in this work we address both problems by incorporating cross-lingual features and knowledge bases from english using cross-lingual links---in this paper , we presented different cross-lingual features that can make use of linguistic properties and knowledge bases of other languages | 1 |
however , the framework of our proposed approach can be generalized to deal with a mix of review texts of more than one products---on reviews of one product , our proposed hlsot approach is easily generalized to labeling a mix of reviews of more than one products | 1 |
we build a 9-gram lm using srilm toolkit with modified kneser-ney smoothing---to address the aforementioned problems of the vsm model , the sentiment vector space model ( s-vsm ) is proposed | 0 |
our word embeddings is initialized with 100-dimensional glove word embeddings---we initialize the embedding layer using embeddings from dedicated word embedding techniques word2vec and glove | 1 |
richardson and domingos propose a method for reasoning about databases and logical constraints using markov random fields---lison et al proposed an approach using markov logic networks to reference resolution | 1 |
the notion of mild context-sensitivity originates in an attempt by [ cite-p-8-3-2 ] to express the formal power needed to define the syntax of natural languages ( nls )---context-sensitivity was formulated in an attempt to express the formal power which is both necessary and sufficient to define the syntax of natural languages | 1 |
dependency parsing is a fundamental task for language processing which has been investigated for decades---dependency parsing consists of finding the structure of a sentence as expressed by a set of directed links ( dependencies ) between words | 1 |
arabic is a morphologically rich language where one lemma can have hundreds of surface forms ; this complicates the tasks of sa---we have presented and analyzed a system for recognizing textual entailment focused primarily on the recognition of false entailment , and demonstrated higher performance than achieved by previous approaches | 0 |
the models are implemented as support vector machine classifiers via the software package svm-light---with such organization , users can easily grasp the overview of product aspects | 0 |
in this paper , we propose a novel method for correcting a deletion error that affects overall understanding of the sentence---in this paper , we have presented a technique for detecting and correcting deletion errors in translated chinese answers | 1 |
we used the srilm toolkit to train a 4-gram language model on the xinhua portion of the gigaword corpus , which contains 238m english words---for the n-gram lm , we use srilm toolkits to train a 4-gram lm on the xinhua portion of the gigaword corpus | 1 |
we propose a language-independent method for the automatic extraction of transliteration pairs from parallel corpora---we proposed a method to automatically extract transliteration pairs from parallel corpora without supervision | 1 |
the clustering method used in this work is latent dirichlet allocation topic modelling---the decoder uses a ckystyle parsing algorithm and cube pruning to integrate the language model scores | 0 |
the feature weights are tuned to optimize bleu using the minimum error rate training algorithm---this work is the first successful application of word embedding techniques for the task of click prediction in sponsored search | 0 |
we implemented this model using the srilm toolkit with the modified kneser-ney discounting and interpolation options---we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing | 1 |
stm is represented by the hierarchical model---vectors can be handled using the theory of hierarchical | 1 |
this study explores the feasibility of performing chinese word segmentation ( cws ) and pos tagging by deep learning---study is among the first ones to perform chinese word segmentation and pos tagging by deep learning | 1 |
lda is a simple model for topic modeling where topic probabilities are assigned words in documents---lda is a completely unsupervised algorithm that models each document as a mixture of topics | 1 |
we trained a 5-gram language model on the english side of each training corpus using the sri language modeling toolkit---for all data sets , we trained a 5-gram language model using the sri language modeling toolkit | 1 |
recently , inversion transduction grammars , namely itg , have been used to constrain the search space for word alignment---discourse segmentation is the task of identifying coherent clusters of sentences and the points of transition between those groupings | 0 |
naive bayes classifier was trained on our corpus and tested on three data sets---naive bayes classifier was trained on our corpus and tested on the three data sets | 1 |
american sign language ( asl ) is a visual/spatial natural language used primarily by the half million deaf individuals in the u.s. and canada---american sign language ( asl ) is a full natural language – with a linguistic structure distinct from english – used as the primary means of communication for approximately one half million deaf people in the united states ( cite-p-15-1-10 ) | 1 |
this framework naturally generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions---costa-jussa and fonollosa considered the source reordering as a translation task which translates the source sentence into reordered source sentence | 0 |
we depend on stanford pos tagger for getting pos tags of the corpus---knowledge graphs , such as freebase , contain a wealth of structured knowledge in the form of relationships between entities and are useful for numerous end applications | 0 |
the language models were built using srilm toolkits---language models were built using the srilm toolkit 16 | 1 |
then we use the stanford parser to determine sentence boundaries---we use pre-trained glove vector for initialization of word embeddings | 0 |
in this paper we confront the task of deciding whether a given term has a positive connotation , or a negative connotation , or has no subjective connotation at all ; this problem thus subsumes the problem of determining subjectivity and the problem of determining orientation---in this paper we confront the task of determining whether a given term has a positive connotation ( e . g . honest , intrepid ) , or a negative connotation ( e . g . disturbing , superfluous ) , or has instead no subjective connotation at all | 1 |
we apply sri language modeling toolkit to train a 4-gram language model with kneser-ney smoothing---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 use kaldi , an open-source speech recognition framework and acoustic models based on the ted-lium corpus and the tedlium 4-gram language model from cantab research---in order to measure translation quality , we use bleu 7 and ter scores | 0 |
we used the stanford lexicalized parser to parse the question---we used the stanford parser to extract dependency features for each quote and response | 1 |
we use the glove vectors of 300 dimension to represent the input words---for all models , we use the 300-dimensional glove word embeddings | 1 |
based on tai et al , miwa and bansal introduced a tree lstm model that can handle different types of children---semantic parsing is the problem of mapping natural language strings into meaning representations | 0 |
also of note , mikolov et al propose a vector offset method to capture syntactic and semantic regularities between word representations learnt by a recurrent neural network language model---we use a popular word2vec neural language model to learn the word embeddings on an unsupervised tweet corpus | 0 |
a 4-grams language model is trained by the srilm toolkit---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 ) | 0 |
part-of-speech ( pos ) tagging is a fundamental natural-language-processing problem , and pos tags are used as input to many important applications---part-of-speech ( pos ) tagging is a fundamental task in natural language processing | 1 |
accurately identifying events in unstructured text is a very difficult task---accurately identifying events in unstructured text is an important goal | 1 |
coreference resolution is the process of linking multiple mentions that refer to the same entity---coreference resolution is a key problem in natural language understanding that still escapes reliable solutions | 1 |
our expert performed best with uncertainty selection , but gained little from suggestions---picked by uncertainty selection , while our non-expert did best with random selection aided by machine label suggestions | 1 |
we implement logistic regression with scikit-learn and use the lbfgs solver---we implement classification models using keras and scikit-learn | 1 |
word sense disambiguation is an important task in natural language processing---word sense disambiguation has been an open problem in computational linguistics | 1 |
for preposition and determiner errors , we construct a system using a phrase-based statistical machine translation framework---for back-translation , we train a phrase-based smt system for each system in reverse direction | 1 |
we apply sri language modeling toolkit to train a 4-gram language model with kneser-ney smoothing---we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus | 1 |
djuric et al use a paragraph2vec approach to classify language on user comments as abusive or clean---djuric et al , 2015 ) also build a binary classifier to classify in between hate speech and clean user comments on a website | 1 |
for the cluster- based method , we use word2vec 2 which provides the word vectors trained on the google news corpus---for all the experiments , we employ word2vec to initialized the word vectors , which is trained on google news with 100 billion words | 1 |
in the first pass , the general information is extracted by segmenting the entire resume into consecutive blocks and each block is annotated with a label indicating its category---in the second pass , the detailed information , such as name and address , are identified in certain blocks ( e . g . blocks labelled with personal information ) , instead of searching globally in the entire resume | 1 |
conditional random fields are undirected graphical models trained to maximize a conditional probability---by jointly modeling and exploiting the context compatibility , the topic coherence and the correlation between them , our model can accurately link all mentions in a document | 0 |
in this paper , we have provided evidence that optimizer instability can have a substantial impact on results---in this paper , we present a series of experiments demonstrating that optimizer instability can account for substantial amount of variation in translation quality | 1 |
for english , rubenstein and goodenough obtained similarity judgements from 51 subjects on 65 noun pairs , a seminal study which was later replicated by miller and charles , and resnik---in the seminal work by rubenstein and goodenough , similarity judgments were obtained from 51 test subjects on 65 noun pairs written on paper cards | 1 |
to solve the above problems , we present one method to exploit non-local information – the trigger feature---in this paper , we exploit non-local features as an estimate of long-distance dependencies | 1 |
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---we train a 4-gram language model on the xinhua portion of the english gigaword corpus using the srilm toolkits with modified kneser-ney smoothing | 1 |
for a more detailed description of the semeval scoring scheme , we refer to mccarthy and navigli---we use srilm for n-gram language model training and hmm decoding | 0 |
in section 3 , we describe the three resources we use in our experiments and how we model them---in section 3 , we describe the three resources we use in our experiments | 1 |
for the hierarchical phrase-based model we used the default moses rule extraction settings , which are taken from chiang---we extract hierarchical rules from the aligned parallel texts using the constraints developed by chiang | 1 |
the alignment template model enhanced phrasal generalizations by using words classes rather than the words themselves---pcdc system must have access to global information regarding the coreference space | 0 |
le and mikolov applied paragraph information into the word embedding technique to learn semantic representation---we use different pretrained word embeddings such as glove 1 and fasttext 2 as the initial word embeddings | 0 |
hatzivassiloglou and mckeown proposed a method for identifying word polarity of adjectives---hatzivassiloglou and mckeown proposed the first method for determining adjective polarities or orientations | 1 |
pang and lee used a subjectivity filter to eliminate the non-subjective sentences in a target movie review , so that they could apply their polarity classifier on a smaller set of higher-quality sentences---in an attempt to get rid of such sentences , pang and lee proposed a pre-processing filter that removes all non-subjective sentences while retaining the subjective ones to be used for sentiment polarity classification | 1 |
our machine translation system is a phrase-based system using the moses toolkit---we trained two 5-gram language models on the entire target side of the parallel data , with srilm | 0 |
part-of-speech ( pos ) tagging is a fundamental language analysis task---most recent approaches use sequenceto-sequence model for paraphrase generation | 0 |
multi-task learning using a related auxiliary task can lead to stronger generalization and better regularized models---multi-task learning has resulted in successful systems for various nlp tasks , especially in cross-lingual settings | 1 |
a different approach to cross-lingual pos tagging is proposed by t盲ckstr枚m et al who couple token and type constraints in order to guide learning---t盲ckstr枚m et al explore the use of mixed type and token annotations in which a tagger is learned by projecting information via parallel text | 1 |
the first and most effective method is to simply use an objective measure of translation quality , such as bleu---this approach attempts to improve translation quality by optimizing an automatic translation evaluation metric , such as the bleu score | 1 |
previous work consistently reported that the wordbased translation models yielded better performance than the traditional methods for question retrieval---we have analyzed the results of the algorithm for the set of nouns in the senseval 2 wsd english lexical sample test bed | 0 |
it was noticed that v-measure tends to favour systems that produce a higher number of clusters than the gold standard and hence is not a reliable estimate of the performance of wsi systems---it was noticed that v-measure tends to favour systems producing a higher number of clusters than the gold standard and hence is not a reliable estimate of the performance of wsi systems | 1 |
we use the mallet implementation of conditional random fields---in particular , we consider conditional random fields and a variation of autoslog | 1 |
phrasebased smt models are tuned using minimum error rate training---the feature weights are tuned to optimize bleu using the minimum error rate training algorithm | 1 |
in this paper , we focus on how to integrate glosses into a unified neural wsd system---in this paper , we seek to address the problem of integrating the glosses | 1 |
dependency parsing is the task to assign dependency structures to a given sentence math-w-4-1-0-14---we measure translation quality via the bleu score | 0 |
this paper describes the online demo of the qualim question answering system---this paper describes the online demo of the qualim 1 question answering system | 1 |
we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings---we use glove word embeddings , an unsupervised learning algorithm for obtaining vector representations of words | 1 |
we can only use simpler word models in these languages---we evaluate several tag models by implementing japanese | 1 |
okuno and mori , 2012 , introduced an ensemble model of wordbased and character-based models for japanese and chinese imes---okuno and mori introduced an ensemble model of word-based and character-based models for japanese and chinese imes | 1 |
this evaluation reveals that the categorial database achieves a high degree of precision and recall---our evaluation reveals that the categorial database achieves a high degree of precision and recall | 1 |
our experiments use the ghkm-based string-totree pipeline implemented in moses---we use the moses smt toolkit to test the augmented datasets | 1 |
bangalore and joshi claimed that if words can be assigned correct supertags , syntactic parsing is almost trivial---bangalore and joshi derived the notion of supertag within the framework of lexicalized tree-adjoining grammars | 1 |
liu , ng , wan , wang , and zhang speculated that vot durations may be affected by tone , as different tones have different fundamental frequencies and pitch levels , which are determined primarily by the tension of the vibrating structure---kiela and bottou showed that such networks learn high-quality representations that can successfully be transfered to natural language processing tasks | 0 |
textual entailment has been proposed as a generic framework for modelling language variability---textual entailment has been proposed as a generic framework for modeling language variability | 1 |
we use the same set of binary features as in previous work on this dataset---cite-p-15-1-13 proposed an automatic method that gives an evaluation result of a translation system as a score | 0 |
to build the semantic space proper , the singular value decomposition was realized with the program svdpackc , and the 300 first singular vectors were retained---to build the lsa space , the singular value decomposition was realized using the program svdpackc , and the first 300 singular vectors were retained | 1 |
sentiment analysis ( sa ) is the task of prediction of opinion in text---sentiment analysis ( sa ) is a hot-topic in the academic world , and also in the industry | 1 |
thus , we can efficiently solve the algorithm by using the hungarian method---this is solved by using the kuhn-munkres algorithm | 1 |
to achieve these goals , we combine two supervised machine learning paradigms , online and multitask learning , adapting and unifying them in a single framework---in isolation , our work tackles them in a single unifying framework based on the combination of two paradigms : online and multitask learning | 1 |
bleu is a precision measure based on m-gram count vectors---the bleu score is based on the geometric mean of n-gram precision | 1 |
to address this challenge , we present a new task : given a sentence with a target entity mention , predict free-form noun phrases that describe appropriate types for the role the target entity plays in the sentence---task : given a sentence with an entity mention , the goal is to predict a set of free-form phrases ( e . g . skyscraper , songwriter , or criminal ) that describe appropriate types for the target entity | 1 |
implementations of left-corner parsers such as that of henderson adopt a arc-standard strategy , essentially always choosing analysis above , and thus do not introduce this kind of local ambiguity---implementations of left-corner parsers such as that of henderson adopt an arc-standard strategy , essentially always choosing analysis , and thus do not introduce this kind of local ambiguity | 1 |
in a second baseline model , we also incorporate 300-dimensional glove word embeddings trained on wikipedia and the gigaword corpus---we use the pre-trained glove 50-dimensional word embeddings to represent words found in the glove dataset | 1 |
dependency parsing is a fundamental task for language processing which has been investigated for decades---dependency parsing is a basic technology for processing japanese and has been the subject of much research | 1 |
we evaluate whether we can combine comments to form larger documents to improve the quality of clusters---we consider whether we can combine comments within a comments dataset to form larger documents to improve the quality of clusters | 1 |
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