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a 5-gram language model with kneser-ney smoothing was trained with srilm on monolingual english data---we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing | 1 |
for both systems , we used the berkeley aligner with default settings to align the parallel data---subsequently , we automatically align the texts at the word level using the berkeley aligner | 1 |
the data collection methods used to compile the dataset used in offenseval are described in zampieri et al---the data collection methods used to compile the dataset provided in offenseval is described in zampieri et al | 1 |
the minimum description length ( mdl ) principle is a method for model selection that provides a generic solution to the overfitting problem ( cite-p-11-1-1 )---minimum description length ( mdl ) principle is a method for model selection that trades off between the explanation of the data by the model | 1 |
we use 300d glove vectors trained on 840b tokens as the word embedding input to the lstm---we use 300-dimensional word embeddings from glove to initialize the model | 1 |
as sentiment analysis in twitter is a very recent subject , it is certain that more research and improvements are needed---for instance , choudhury et al predicted the onset of depression from user tweets , while other studies have modeled distress | 0 |
this paper describes a system for navigating europeana , an aggregation of collections of cultural heritage artefacts---this paper describes a system for navigating large collections of information about cultural heritage | 1 |
we used the glove embeddings for these features---we used 300-dimensional pre-trained glove word embeddings | 1 |
morpa is a fully implemented parser developed for use in a text-to-speech conversion system---moreover , there are several types of conversational humor which are employed in human conversation | 0 |
we use wikipedia item categories and the wordnet ontology for identifying entities from each subcategory---for each word w t i , we use wordnet to find its corresponding synonyms | 1 |
to obtain the synonym of a word , we first label the words with their part-ofspeech using the stanford pos tagger---we train distributional similarity models with word2vec for the source and target side separately | 0 |
sentiment analysis ( sa ) is the task of analysing opinions , sentiments or emotions expressed towards entities such as products , services , organisations , issues , and the various attributes of these entities ( cite-p-9-3-3 )---sentiment analysis ( sa ) is the determination of the polarity of a piece of text ( positive , negative , neutral ) | 1 |
based on above work , we explore neural pre- and in-parsing models for ecd---an annotation effort demonstrates implicit relations reveal as much as 30 % of meaning | 0 |
in this paper we described the system submitted for the semeval 2014 task 9 ( sentiment analysis in twitter )---in this paper we describe the system submitted for the semeval 2014 task 9 ( sentiment analysis in twitter ) | 1 |
these word vectors can be randomly initialized from a uniform distribution , or be pre-trained from text corpus with embedding learning algorithms---the embedded word vectors are trained over large collections of text using variants of neural networks | 1 |
in this paper , mencius incorporates a rule-based knowledge representation and template-matching tool , infomap , into a maximum entropy framework---mencius , the chinese named entity recognizer presented here , incorporates a rule-based knowledge representation and a template-matching tool , called infomap , into a maximum entropy framework | 1 |
using espac medlineplus , we trained an initial phrase-based moses system---we use the moses toolkit to train our phrase-based smt models | 1 |
we describe our process for efficient annotation , and present the first quantitative analysis of arabic morphosyntactic phenomena---we describe a process for how to do the annotation efficiently ; and furthermore , present the first quantitative analysis of morphosyntactic phenomena in arabic | 1 |
note that we employ negative sampling to transform the objective---we use negative sampling to approximate softmax in the objective function | 1 |
the discriminative parser we used in this paper is based on the part-factored model and features of the mstparser---the projected lexical features that we propose in this work are based on lexicalized versions of features found in mstparser , an edge-factored discriminative parser | 1 |
we used the moses pbsmt system for all of our mt experiments---we used moses as the implementation of the baseline smt systems | 1 |
conversation is a joint social process , with participants cooperating to exchange information---some language-specific properties in chinese have impact on errors | 0 |
we used a 4-gram language model which was trained on the xinhua section of the english gigaword corpus using the srilm 4 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 |
this model is inspired by formalisms based on structural features like head-driven phrase structure grammar---the grammar matrix is couched within the head-driven phrase structure grammar framework | 1 |
ngram features have been generated with the srilm toolkit---kennedy and inkpen explore negation shifting by incorporating negation bigrams as additional features into machine learning approaches | 0 |
the feature weights for each system were tuned on development sets using the moses implementation of minimum error rate training---finally , the ape system was tuned on the development set , optimizing ter with minimum error rate training | 1 |
word sense disambiguation ( wsd ) is a widely studied task in natural language processing : given a word and its context , assign the correct sense of the word based on a predefined sense inventory ( cite-p-15-3-4 )---word sense disambiguation ( wsd ) is a problem of finding the relevant clues in a surrounding context | 1 |
the presence of many named entities leads the performance of the system to plummet greatly---in our experiments , learning from implicit supervision alone is not a viable strategy for algebra word problems | 0 |
the aim of sentence-level qe is to predict human mediated translation edit rate scores that are obtained by comparing the mt output to its post-edited version---the sentence-level qe task aims at predicting human mediated translation edit rate between the raw mt output and its manually post-edited version | 1 |
note , however , that shieber 's algorithm is still better than parsing the object grammar---in our experiments of unsupervised dependency grammar learning , we show that unambiguity regularization is beneficial to learning , and by incorporating regularization strength annealing and sparsity priors | 0 |
recent work addresses this problem by scoring a particular dimension of essay quality such as coherence , technical errors , organization , and thesis clarity---situated question answering is a challenging problem that requires reasoning about uncertain interpretations of both a question and an environment together with background knowledge to determine the answer | 0 |
in all of our experiments , our models contain a lookup table by employing word2vec trained on google news , which comprises more than 100b words with a vocabulary size of around 3m---for all the experiments , we employ word2vec to initialized the word vectors , which is trained on google news with 100 billion words | 1 |
we used the pre-trained word embeddings that were learned using the word2vec toolkit on google news dataset---as input to the encoder , we downloaded pre-trained 300-dimensional embeddings trained on google news data using the word2vec software | 1 |
for the language model , we used sri language modeling toolkit to train a trigram model with modified kneser-ney smoothing on the 31 , 149 english sentences---we used kenlm with srilm to train a 5-gram language model based on all available target language training data | 1 |
we use stanford corenlp for pos tagging and lemmatization---for pos tagging and syntactic parsing , we use the stanford nlp toolkit | 1 |
specifically , we used the python scikit-learn module , which interfaces with the widely-used libsvm---for nb and svm , we used their implementation available in scikit-learn | 1 |
neubig et al proposed to train btg parsers for preordering by regarding btg trees behind word reordering as latent variables , and we use latent variable perceptron together with beam search---neubig et al proposed to train a discriminative btg parser for preordering directly from word-aligned parallel text by handling underlying parse trees with latent variables | 1 |
attention has recently been used with considerable empirical success in tasks such as translation and image caption generation---rnn has proven to be successful in natural language processing tasks such as machine translation , automated essay scoring , and question answering | 1 |
we pre-train the word embedding via word2vec on the whole dataset---we use the word2vec tool to pre-train the word embeddings | 1 |
we used the srilm toolkit to train a 4-gram language model on the english side of the training corpus---we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing | 1 |
dependency parsing is a crucial component of many natural language processing systems , for tasks such as text classification ( o ? zgu ? r and gu ? ngo ? r , 2010 ) , statistical machine translation ( cite-p-13-3-0 ) , relation extraction ( cite-p-13-1-1 ) , and question answering ( cite-p-13-1-3 )---dependency parsing is the task of building dependency links between words in a sentence , which has recently gained a wide interest in the natural language processing community and has been used for many problems ranging from machine translation ( cite-p-12-1-4 ) to question answering ( zhou et al. , 2011a ) | 1 |
we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing---relation extraction is a fundamental task that enables a wide range of semantic applications from question answering ( cite-p-13-3-12 ) to fact checking ( cite-p-13-3-10 ) | 0 |
we use skip-gram with negative sampling for obtaining the word embeddings---in addition , we utilize the pre-trained word embeddings with 300 dimensions from for initialization | 1 |
we use the pre-trained word2vec embeddings provided by mikolov et al as model input---for this purpose , we use the moses toolkit for training translation models and decoding , as well as srilm 2 to build the language models | 0 |
in this paper , we propose a generic method that aims to be independent of the characteristics described above ( use of search terms or sentiment analysis tools )---in this paper , we present a novel approach which performs high quality filtering automatically , through modelling not just words | 1 |
semantic role labeling ( srl ) is the task of identifying the predicate-argument structure of a sentence---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 ) | 1 |
we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus---we train a 5-gram language model with the xinhua portion of english gigaword corpus and the english side of the training set using the srilm toolkit | 1 |
language models were built with srilm , modified kneser-ney smoothing , default pruning , and order 5---the language models used were 7-gram srilm with kneser-ney smoothing and linear interpolation | 1 |
similarly , somasundaran et al utilized a svm classifier to recognize opinionated sentences---somasundaran et al , 2007 ) argues that making finer grained distinction of subjective types further improves the qa system | 1 |
yan et al proposed a biterm topic model , which assumes that a wordpair is independently drawn from a specific topic---yan et al presented a variant of lda , dubbed biterm topic model , especially for short text modeling to alleviate the problem of sparsity | 1 |
our evaluation metric is case-insensitive bleu-4---the evaluation metric is casesensitive bleu-4 | 1 |
our results show that word prediction can increase aac communication rate and that more accurate predictions significantly improve communication rate---prediction method demonstrates that further improvements in language modeling for word prediction are likely to appreciably increase communication rate | 1 |
in the probabilistic formulation , the task of learning taxonomies from a corpus is seen as a probability maximization problem---in the probabilistic formulation , the task of learning taxonomies from a corpus is seen as a maximum likelihood problem | 1 |
the word embeddings can provide word vector representation that captures semantic and syntactic information of words---in addition , recently-studied semantic word embeddings , eg , word2vec , can capture the semantics | 1 |
we use the srilm toolkit to compute our language models---we used srilm -sri language modeling toolkit to train several character models | 1 |
pereira et al cluster nouns according to their distribution as direct objects of verbs , using information-theoretic tools---word embeddings are initialized with pretrained glove vectors 1 , and updated during the training | 0 |
we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus---however , we use a large 4-gram lm with modified kneser-ney smoothing , trained with the srilm toolkit , stolcke , 2002 and ldc english gigaword corpora | 1 |
we implemented the different aes models using scikit-learn---for training our system classifier , we have used scikit-learn | 1 |
a pun is the exploitation of the various meanings of a word or words with phonetic similarity but different meanings---coreference resolution is the task of partitioning a set of mentions ( i.e . person , organization and location ) into entities | 0 |
the approach remains equally successful on sts 2014 data---for several test sets , supervised systems were the most successful in sts 2012 | 1 |
relation extraction is the key component for building relation knowledge graphs , and it is of crucial significance to natural language processing applications such as structured search , sentiment analysis , question answering , and summarization---relation extraction is the task of automatically detecting occurrences of expressed relations between entities in a text and structuring the detected information in a tabularized form | 1 |
smith and eisner propose structural annealing , in which a strong bias for local dependency attachments is enforced early in learning , and then gradually relaxed---transe is only suitable for 1-to-1 relations , there remain flaws for 1-to-n , n-to-1 and n-to-n relations | 0 |
for the evaluation of the results we use the bleu score---we substitute our language model and use mert to optimize the bleu score | 1 |
to enhance the attention mechanism , implicit word reordering knowledge needs to be incorporated into attention-based nmt---for the first two features , we adopt a set of pre-trained word embedding , known as global vectors for word representation | 0 |
for this task , we use the widely-used bleu metric---we use three common evaluation metrics including bleu , me-teor , and ter | 1 |
the word embeddings can provide word vector representation that captures semantic and syntactic information of words---with word embeddings , each word is linked to a vector representation in a way that captures semantic relationships | 1 |
abstract meaning representation is a semantic formalism in which the meaning of a sentence is encoded as a rooted , directed , acyclic graph---abstract meaning representation is a semantic formalism which represents sentence meaning in a form of a rooted directed acyclic graph | 1 |
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---socher et al learned vector space representations for multi-word phrases using recursive autoencoders for the task of sentiment analysis | 1 |
we estimate the parameters by maximizingp using the expectation maximization algorithm---for training the trigger-based lexicon model , we apply the expectation-maximization algorithm | 1 |
specific properties of the english language are visible in user manuals that have been translated to other languages from english---tokenization of the english data was done using the berkeley tokenizer | 0 |
word embeddings have shown promising results in nlp tasks , such as named entity recognition , sentiment analysis or parsing---more recently , features drawn from word embeddings have been shown to be effective in various text classification tasks such as sentiment analysis and named entity recognition | 1 |
similarly , hua wu applied synonyms relationship between two different languages to automatically acquire english synonymous collocation---similarly , hua applied synonyms relationships between two different languages to automatically acquire english synonymous collocations | 1 |
our framework is motivated by distant supervision for learning relation extraction models---we obtain these dependency constructions by implementing a distantly supervised pattern extraction approach | 1 |
to unravel this problem , we learn selectional preferences from a large raw corpus , and incorporate them into a sota pas analysis model , which considers the consistency of all pass in a given sentence---the distributional pattern or dependency with syntactic patterns is also a prominent source of data input | 0 |
we assume that the percentages of these math-w-2-4-1-24 mappings are relatively low---we suspect that this is due to some problems caused by the math-w-10-1-0-94 mapping objects | 1 |
soricut and marcu introduce a statistical discourse segmenter , which is trained on rst-dt to label words with boundary or no-boundary labels---soricut and marcu use a standard bottomup chart parsing algorithm to determine the discourse structure of sentences | 1 |
we train the cbow model with default hyperparameters in word2vec---we use word2vec from as the pretrained word embeddings | 1 |
collobert et al reported that word embeddings learned from significant amounts of unlabeled data are far more satisfactory than the randomly initialized embeddings---collobert et al , 2011 ) reported that word embeddings learned from significant amounts of unlabeled data are far more satisfactory than the randomly initialized embeddings | 1 |
such measures as mutual information has been proposed to evaluate word semantic similarity based on the co-occurrence information on a large corpus---such measures as mutual information , latent semantic analysis , log-likelihood ratio have been proposed to evaluate word semantic similarity based on the co-occurrence information on a large corpus | 1 |
we used the phrase-based smt model , as implemented in the moses toolkit , to train an smt system translating from english to arabic---we experimented using the standard phrase-based statistical machine translation system as implemented in the moses toolkit | 1 |
we also use a 4-gram language model trained using srilm with kneser-ney smoothing---we used srilm to build a 4-gram language model with kneser-ney discounting | 1 |
this kind of knowledge is useful not only for stance detection across multiple topics but also for various real-world applications including public opinion surveys , electoral predictions , electoral campaigns , and online debates---for the feature-based system we used logistic regression classifier from the scikit-learn library | 0 |
in section 4 , we present our view of analyzing the structure of task-oriented human-human dialogs---in this work , we aim to learn a semantic parser that maps a natural language question | 0 |
this approach was pioneered by sch眉tze using second order co-occurrences to construct the context representation---sch眉tze created sense representations by clustering context representations derived from co-occurrence | 1 |
the induced grammars can be used to construct large treebanks , study language acquisition , etc---the induced grammars can be used to construct large treebanks , study language acquisition , improve machine translation , and so on | 1 |
we use bleu scores to measure translation accuracy---the function word feature set consists of 318 english function words from the scikit-learn package | 0 |
neural network language models , or continuous-space language models ( cslms ) , have been shown to improve the performance of statistical machine translation ( smt ) when they are used for reranking n-best translations---recently , neural network language models , or continuous-space language models ( cslms ) ( cite-p-12-1-1 , cite-p-12-3-3 , cite-p-12-1-10 ) are being used in statistical machine translation ( smt ) | 1 |
in collobert et al the authors proposed a deep neural network , which learns the word representations and produces iobes-prefixed tags discriminatively trained in an end-to-end manner---in collobert et al , the authors proposed a unified cnn architecture to tackle various nlp problems traditionally handled with statistical approaches | 1 |
the language model pis implemented as an n-gram model using the srilm-toolkit with kneser-ney smoothing---the probabilistic language model is constructed on google web 1t 5-gram corpus by using the srilm toolkit | 1 |
experiments show that our proposed model outperforms the standard attention-based neural machine translation baseline---experiments show that our proposed model significantly improves the translation performance | 1 |
the optimization of the weights of the model with the additional translation options is trained with mert against the bleu evaluation metric on the newscommentaries 2012 4 set---the weights 位 m in the log-linear model were trained using minimum error rate training with the news 2009 development set | 1 |
a popular measure of this association is pointwise mutual information---the classical method is pointwise mutual information | 1 |
the bell tree represents the search space of the coreference resolution problem---which uses the bell tree to represent the search space | 1 |
word embeddings are dense vector representations of words---we used mecab 4 and the stanford chinese segmenter 5 to segment japanese and chinese sentences | 0 |
experimental results show that the mt error prediction accuracy is increased from 69.1 to 72.2 in f-score---with word posterior probability and target pos context ( cite-p-19-1-26 ) , the mt error prediction accuracy is increased from 69 . 1 to 72 . 2 in f-score | 1 |
semantic role labeling ( srl ) is the task of identifying the semantic arguments of a predicate and labeling them with their semantic roles---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 ) | 1 |
for dependency parsing , the gain is more pronounced : almost 2 % over the full training set---for dependency parsing , the improvement reaches 2 percent points over the full training baseline | 1 |
we leave exploring such models in combination with multi-task learning for future work---in future work , we want to explore alternative multi-task learning | 1 |
dependency parsing is a core task in nlp , and it is widely used by many applications such as information extraction , question answering , and machine translation---fung et al also proposed a similar approach that uses vector-space model and takes a bilingual lexicon as feature set to estimate the similarity between a word and its translation candidates | 0 |
for example , minimum bayes risk decoding over n-best list tries to find a hypothesis with lowest expected loss with respect to all the other translations , which can be viewed as sentence-level consensus-based decoding---for example , minimum bayes risk decoding over n-best list finds a translation that has lowest expected loss with all the other hypotheses , and it shows that improvement over the maximum a posteriori decoding | 1 |
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