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we used kenlm with srilm to train a 5-gram language model based on all available target language training data---we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing | 1 |
we present a novel , unsupervised , and distance measure agnostic method for search space reduction in spell correction using neural character embeddings---in this paper , we propose a novel , unsupervised , distance measure agnostic , highly accurate , method of search space reduction for spell correction | 1 |
we also use the stanford ner tagger to identify named entities within the np---for the character-based model we use publicly available pre-trained character embeddings 3 de- rived from glove vectors trained on common crawl | 0 |
thus , dependency parsing relies heavily on the lexical information of words---dependency parsing can not utilize phrase categories , and thus relies on word information | 1 |
further improvements in the original feature set and the induction algorithm , as well as full integration in decoding are needed to potentially result in substantial performance improvements---and the induction algorithm , as well as full integration in decoding are needed to potentially result in substantial performance improvements | 1 |
note that two-level attention mechanisms have also been used in the context of summarization ( cite-p-19-3-4 ) , document classification ( cite-p-19-3-15 ) , dialog systems ( cite-p-19-3-10 ) , etc---note that such two-level attention mechanisms ( cite-p-19-3-4 , cite-p-19-3-15 , cite-p-19-3-10 ) have been used in the context of unstructured data ( as opposed to structured data | 1 |
coreference resolution is the task of clustering referring expressions in a text so that each resulting cluster represents an entity---coreference resolution is the task of determining which mentions in a text are used to refer to the same real-world entity | 1 |
in order to reduce the source vocabulary size translation , the german text was preprocessed by splitting german compound words with the frequency-based method described in---for the translation from german into english , german compounds were split using the frequencybased method described in | 1 |
in this bakeoff , our basic model is based on the framework described in the work of ratnaparkhi which was applied for english pos tagging---undersampling causes negative effects on active learning | 0 |
the feature weights for each system were tuned on development sets using the moses implementation of minimum error rate training---all model weights were trained on development sets via minimum-error rate training with 200 unique n-best lists and optimizing toward bleu | 1 |
a 4-gram language model is trained on the xinhua portion of the gigaword corpus with 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 |
additionally , coreference resolution is a pervasive problem in nlp and many nlp applications could benefit from an effective coreference resolver that can be easily configured and customized---the log-linear model features weights are tuned using the newswire part of nist mt06 as the tuning dataset and bleu as the objective function | 0 |
sentiment classification is the task of identifying the sentiment polarity of a given text---sentiment classification is a special task of text categorization that aims to classify documents according to their opinion of , or sentiment toward a given subject ( e.g. , if an opinion is supported or not ) ( cite-p-11-1-2 ) | 1 |
in order to overcome data sparseness , we used techniques borrowed from latent semantic indexing observed between terms which are related but do not co-occur---in our earlier experiments , we used latent semantic analysis for dimensionality reduction in an attempt to automatically cluster words that are semantically similar | 1 |
liu and gildea proposed stm , a structural approach based on syntax to addresses the failure of lexical similarity based metrics in evaluating translation grammaticality---liu and gildea propose stm , a metric based on syntactic structure , that addresses the failure of lexical similarity based metrics to evaluate translation grammaticality | 1 |
we used the svm implementation provided within scikit-learn---we use the svm implementation from scikit-learn , which in turn is based on libsvm | 1 |
burkett and klein and burkett et al made efforts to do joint parsing and alignment---burkett and klein and burkett et al focused on joint parsing and alignment | 1 |
the universal dependencies project seeks to develop cross-linguistically consistent treebank annotation of morphology and syntax for many languages---for the second step , sentence selection adopts a particular strategy to choose content | 0 |
again for the ¡°complete¡± model , we checked the top 20 answer candidates that ranked higher than the actual ¡°correct¡± one---for the ¡° complete ¡± model , we checked the top 20 answer candidates that ranked higher than the actual ¡° correct ¡± | 1 |
with english gigaword corpus , we use the skip-gram model as implemented in word2vec 3 to induce embeddings---we first train a word2vec model on fr-wikipedia 11 to obtain non contextual word vectors | 1 |
we first train a word2vec model on fr-wikipedia 11 to obtain non contextual word vectors---位 8 are tuned by minimum error rate training on the dev sets | 0 |
notable discriminative approaches are conditional random fields and structural svm---the most popular methods in this context , in particular , are hidden markov models and conditional random fields | 1 |
we used the svm implementation of scikit learn---we use the scikit-learn toolkit as our underlying implementation | 1 |
as embedding vectors , we used the publicly available representations obtained from the word2vec cbow model---we derive 100-dimensional word vectors using word2vec skip-gram model trained over the domain corpus | 1 |
this can be partly explained by the politeness theory---the third feature type is based on the politeness theory | 1 |
in this work , we study the performance and behavior of two neural statistical language models so as to highlight some important caveats of the classical training algorithms---in this work , we proposed three new methods for training neural network language models and showed their efficiency both in terms of computational complexity and generalization performance | 1 |
the 50-dimensional pre-trained word embeddings are provided by glove , which are fixed during our model training---cite-p-17-3-2 proposed a recursive neural network designed to model the subtrees , and cnn to capture | 0 |
relation extraction is the task of detecting and classifying relationships between two entities from text---the weights for these features are optimized using mert | 0 |
for this model , we use a binary logistic regression classifier implemented in the lib-linear package , coupled with the ovo scheme---we use the wrapper of the scikit learn python library over the liblinear logistic regression implementation | 1 |
following , we lower-case the text and remove all punctuations and partial words 1---as suggested by the in section 2 , relationals occur closer to the than qualitatives , so this result is consistent | 0 |
our scoring procedure uses the ted algorithm defined by zhang and shasha---our implementation is based on the dynamic programming algorithm of zhang and shasha | 1 |
we separately test on two datasets , the fce test set and the conll-2014 test set---our test sets are the conll 2014 evaluation set and the jfleg test set | 1 |
then we apply the max-over-time pooling to get a single vector representation---we then apply a max-over-time pooling operation over the feature map | 1 |
semantic role labeling ( srl ) is the task of identifying the predicate-argument structure of a sentence---semantic role labeling ( srl ) consists of finding the arguments of a predicate and labeling them with semantic roles ( cite-p-9-1-5 , cite-p-9-3-0 ) | 1 |
we use a combination of structures derived from phrase structure trees and dependency trees---while reranking has benefited many tagging and parsing tasks including semantic role labeling , it has not yet been applied to semantic parsing | 0 |
we use the attention-based nmt model introduced by bahdanau et al as our text-only nmt baseline---we briefly describe the baseline attention-based nmt based on previous work that we used | 1 |
cite-p-24-3-10 introduced latent responding factors to model multiple responding mechanisms---cite-p-24-3-1 presented a conditional variational framework for generating specific responses | 1 |
in this paper , we are interested in uncertainty sampling for pool-based active learning , in which an unlabeled example x with maximum uncertainty is selected to augment the training data at each learning cycle---bracketing transduction grammar is a special case of synchronous context free grammar | 0 |
in place of surface-based givenness checks , as a first step in this direction we developed an approach integrating distributional semantics to check whether a word in a sentence is similar enough to a word in the context to count as given---work , we developed an approach based on distributional semantics to check whether a word in an answer is similar enough to a word in the question to count as given | 1 |
the examples are flesch reading ease score , fog index , fry graph , smog etc---word alignment is a critical first step for building statistical machine translation systems | 0 |
relation extraction is the task of finding relationships between two entities from text---metaphors are marked by their unusualness in a given context | 0 |
data mining on appraisal expressions gives meaningful and non-obvious insights---data mining applied to appraisal expressions can yield insights into public opinion | 1 |
active learning also has been applied to many nlp applications , including pos tagging and pars-ing---active learning has been applied to statistical parsing to improve sample selection for manual annotation | 1 |
conneau et al proposed the model which is trained using glove word embeddings---pennington et al combine both methods in the glove word embeddings | 1 |
we use the stanford parser with stanford dependencies---we used the stanford parser to generate dependency trees of sentences | 1 |
based on topic models , xiao et al present a topic similarity model for hpb system , where each rule is assigned with a topic distribution---xiao et al propose a topic similarity model which incorporates the rule-topic distributions on both the source and target side into a hierarchical phrase-based system for rule selection | 1 |
we evaluated the translation quality of the system using the bleu metric---for the evaluation , we used bleu , which is widely used for machine translation | 1 |
in this paper , we present a novel approach for identifying argumentative discourse structures in persuasive essays---they discriminate learners ’ proficiency adequately trained on error patterns extracted from an esl corpus , and can generate exclusive distractors with taking context of a given sentence into consideration | 0 |
1 this research was supported by nsf grants # iri-9010112 and # iri-9416916 , the nemours foundation , a unidel summer research fellowship from the department of computer and information sciences at the university of delaware , and nsf graduate traineeship grant # ger-9354869---1 this research was supported by nsf grants # iri-9010112 and # iri-9416916 , the nemours foundation , a unidel summer research fellowship from the department of computer and information sciences | 1 |
our normalization approach is based on continuous distributed word vector representations , namely the state-of-the-art method word2vec---we complement the neural approaches with a simple neural network that uses word representations , namely a continuous bag-of-words model | 1 |
experiments on both english and chinese affective lexicons show that the proposed method yielded a smaller error rate than the pagerank , kernel and linear regression methods---experiments on both english and chinese affective lexicons show that the proposed method yielded a smaller error rate on va prediction than the linear regression , kernel method , and pagerank algorithm | 1 |
the language model was smoothed with the modified kneser-ney algorithm as implemented in , and we only kept 4-grams and 5-grams that occurred at least three times in the training data---the language model was smoothed with the modified kneser-ney algorithm as implemented in srilm , and we only kept 4-grams and 5-grams that occurred at least three times in the training data | 1 |
we adopt a long short-term memory network for the word-level and sentence-level feature extraction---we use the long short-term memory architecture for recurrent layers | 1 |
in this paper , we proposed a novel , unsupervised , distance-measure agnostic method of search space reduction for spell correction---we present a novel , unsupervised , and distance measure agnostic method for search space reduction in spell correction | 1 |
we use the pre-trained 300-dimensional word2vec embeddings trained on google news 1 as input features---we perform pre-training using the skip-gram nn architecture available in the word2vec 13 tool | 1 |
we train and evaluate our model on the english corpus of the conll-2012 shared task---we evaluate our approach on the english portion of the conll-2012 dataset | 1 |
relation extraction is a traditional information extraction task which aims at detecting and classifying semantic relations between entities in text ( cite-p-10-1-18 )---relation extraction 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 |
a standard sri 5-gram language model is estimated from monolingual data---a 4-gram language model was trained on the monolingual data by the srilm toolkit | 1 |
moreover , our method employs predicate inversion and repetition to resolve the problem that japanese has a predicate at the end of a sentence---in japanese spoken language , our method takes advantage of a predicate inversion to resolve the problem that japanese has the predicate at the end of a sentence | 1 |
in this paper , we propose to learn continuous word embeddings with metadata of category information within cqa pages for question retrieval---in this paper , we present a general method to leverage the metadata of category information within cqa pages to further improve the word embedding representations | 1 |
we use logistic regression , support vector machines and neural networks with long short-term memory units for the different classification prob-lems---for sequence modeling in all three components , we use the long short-term memory recurrent neural network | 1 |
our results show that simple fixed-length truncation methods with high limits ( such as taking the first 10 letters ) improves summarization scores---our results show that a simple fixed-length word truncation approach performs slightly better than no stemming , whereas applying complex morphological | 1 |
in this paper , we show that semi-supervised viterbi-em can be used to extend the lexicon of a generative ccg parser---sentiment classification is a well-studied and active research area ( cite-p-20-1-11 ) | 0 |
furthermore , we train a 5-gram language model using the sri language toolkit---we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting | 1 |
all smt models were developed using the moses phrase-based mt toolkit and the experiment management system---the smt systems were trained using the moses toolkit and the experiment management system | 1 |
this paper reports on work in progress on an exemplar activation model as an alternative to one-vector-per-word approaches to word meaning in context---in this paper , we present an exemplar-based distributional model for modeling word meaning in context | 1 |
moreover , it yields state-of-the art performance for a majority of languages---and yields state-of-the-art performance for a majority of languages | 1 |
to train the model , we use the averaged perceptron with the early update---to achieve efficient parsing , we use a beam search strategy like the previous methods | 1 |
we use the stanford part of speech tagger to annotate each word with its pos tag---however , much of this work has relied on multiple segmenters that perform differently on the same input | 0 |
the hierarchical phrase-based model has been widely adopted in statistical machine translation---the hierarchical phrase-based translation model has been widely adopted in statistical machine translation tasks | 1 |
we use a fourgram language model with modified kneser-ney smoothing as implemented in the srilm toolkit---we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting | 1 |
we used a 5-gram language model with modified kneser-ney smoothing , built with the srilm toolkit---we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
to train our models , we use svm-light-tk 15 , which enables the use of structural kernels in svm-light---we used svm-light-tk , which enables the use of the partial tree kernel | 1 |
in our experiments , we evaluate our model on the semeval-2010 task 8 dataset , which is one of the most widely used benchmarks for relation classification---we evaluated our model on the semeval-2010 task 8 dataset , which is an established benchmark for relation classification | 1 |
we train a 4-gram language model on the xinhua portion of english gigaword corpus by srilm toolkit---a 4-gram language model is trained on the monolingual data by srilm toolkit | 1 |
the google n-gram corpus has been applied to many nlp tasks such as spelling correction , multi-word expression classification and lexical disambiguation---the corpus has been used for many tasks such as spelling correction and multi-word expression classification | 1 |
in this paper , we propose an interactive group creating system for twitter---in this paper , we have proposed an interactive group creation system for twitter | 1 |
the tuning process was done using mert with minimum bayes-risk decoding on moses and focusing on minimizing the bleu score of the development set---system tuning was carried out using both k-best mira and minimum error rate training on the held-out development set | 1 |
relation extraction is a crucial task in the field of natural language processing ( nlp )---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 | 1 |
stance detection has been defined as automatically detecting whether the author of a piece of text is in favor of the given target or against it---stance detection is the task of automatically determining from text whether the author of the text is in favor of , against , or neutral towards a proposition or target | 1 |
in tables 1 and 2 , we compare our results with those obtained by ( cite-p-16-1-11 ) on different models---in tables 1 and 2 , we compare our results with those obtained by ( cite-p-16-1-11 ) | 1 |
following cite-p-10-3-0 , the parameter math-w-6-7-0-19 is set to be some constant math-w-6-7-0-28 that is typically chosen through optimization over the development set---a , the highest scoring string under the model is math-p-2-2-0 where math-w-2-3-0-1 is some value that reflects the relative importance of the language model ; math-w-2-3-0-18 is typically chosen by optimization | 1 |
owczarzak et al presented a method using the lexical-functional grammar dependency tree---the syntax-based metric proposed by owczarzak et al uses the lexical-functional grammar dependency tree | 1 |
word embeddings are usually trained assuming that semantically-similar words occur within the same textual contexts---distributional semantics is based on the theory that semantically similar words occur within the same textual contexts | 1 |
semantic parsing is the problem of deriving a structured meaning representation from a natural language utterance---semantic parsing is the task of mapping a natural language ( nl ) sentence into a completely formal meaning representation ( mr ) or logical form | 1 |
tree kernels have been used in traditional re and have helped achieve state of the art performance ( cite-p-16-1-7 , cite-p-16-1-2 , cite-p-16-1-15 , cite-p-16-1-14 , cite-p-16-3-1 )---tree kernel models are the basis for the current state of the art ( cite-p-16-1-7 , cite-p-16-1-2 , cite-p-16-1-15 , cite-p-16-1-14 , cite-p-16-3-1 ) | 1 |
in order to find the correct sentence index in the page , we used the hungarian algorithm to find the matching sentences---we use srilm for n-gram language model training and hmm decoding | 0 |
we exploit the svmlight-tk toolkit for kernel computation---our system uses the svm-light-tk toolkit 3 for computation of the hybrid kernels | 1 |
mcclosky et al used self-training for english constituency parsing---mcclosky et al used self-training for constituency parsing | 1 |
our model is a structured conditional random field---in this paper , we investigate the possibility of using naturally annotated emoji-rich twitter data | 0 |
we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting---we use srilm to train a 5-gram language model on the target side of our training corpus with modified kneser-ney discounting | 1 |
hassan et al proposed an error correction system that use a finite state automata to propose candidate corrections for wrong words , then assign a score to each candidate and choose the best correction based on the context---hassan et al used a finite state automata to propose candidates corrections , then assign a score to each candidate and choose the best correction in the context | 1 |
this paper presents a novel approach to automated sentence completion based on pointwise mutual information ( pmi )---in this paper , a model based on pointwise mutual information ( pmi ) is proposed to measure the degree of association between answer options and other sentence | 1 |
second , we enhance the neural network architecture by using tensor layers , which allows us to model richer interactions---second , we augment the architecture of the neural network with tensor layers that capture important higher-order interaction | 1 |
sixteen teams from three continents participated in this task---sixteen teams from three continents participated in the conll-2015 shared task | 1 |
much current work in discourse parsing focuses on the labelling of discourse relations , using data from the penn discourse treebank---the penn discourse treebank corpus is the best-known resource for obtaining english connectives | 1 |
travel blogs are considered a useful information source for obtaining travel information , because many bloggers ' travel experiences are written in this form---travel blogs are a useful information source for obtaining travel information , because many bloggers ' travel experiences are written in this form | 1 |
to generate dependency links , we use the stanford pos tagger 18 and the malt parser---we use the stanford pos tagger to obtain the lemmatized corpora for the parss task | 1 |
the lexical cohesion relations of reiteration and collocation are used to identify related words---as word vectors the authors use word2vec embeddings trained with the skip-gram model | 0 |
we use case-sensitive bleu-4 to measure the quality of translation result---we measure machine translation performance using the bleu metric | 1 |
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