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in our trained model , the supported_by feature also has a high positive weight for “ on ”---in our trained model , the supported _ by feature also has a high positive weight for “ | 1 |
we use bleu scores to measure translation accuracy---we use case-sensitive bleu to assess translation quality | 1 |
we further used adam to optimize the parameters , and used cross-entropy as the loss function---we used adam optimization with the original parameters that are the default , and the loss function used is cross-entropy | 1 |
to train a crf model , we use the wapiti sequence labelling toolkit---we employ the crf implementation in the wapiti toolkit , using default settings | 1 |
in section 3 we show that answer accuracy is strongly correlated with the log-likelihood of the qa pairs computed by this statistical model---we demonstrate that there is a strong correlation between the question answering ( qa ) accuracy and the log-likelihood of the answer | 1 |
blitzer et al induced a correspondence between features from a source and target domain based on structural correspondence learning over unlabelled target domain data---using a different approach , blitzer et al induces correspondences between feature spaces in different domains , by detecting pivot features | 1 |
our model substantially outperforms a state-of-the-art semantic parsing baseline , yielding a 29 % absolute improvement in accuracy---pairs show that our model significantly outperforms baselines based on a state-of-the-art model | 1 |
as is the case with the multi-task system , we apply the cross entropy loss function and the adam optimizer to train the energybased network---for the first lstm model , we use softmax as our non-linear function and optimize the categorical cross entropy loss using adam | 1 |
wordnet is a comprehensive lexical resource for word-sense disambiguation ( wsd ) , covering nouns , verbs , adjectives , adverbs , and many multi-word expressions---wordnet is a general english thesaurus which additionally covers biological terms | 1 |
furthermore , by combining these extracted causality and contradiction pairs , we can generate one million plausible causality hypotheses that are not written in any single sentence in our corpus with reasonable precision---by combining these extracted causality pairs and contradiction pairs , we generated one million plausible causality hypotheses that were not written in any single sentence in our corpus with reasonable precision | 1 |
coreference resolution is the task of determining which mentions in a text refer to the same entity---coreference resolution is a key problem in natural language understanding that still escapes reliable solutions | 1 |
coreference resolution is the task of clustering a set of mentions in the text such that all mentions in the same cluster refer to the same entity---although coreference resolution is a subproblem of natural language understanding , coreference resolution evaluation metrics have predominately been discussed in terms of abstract entities and hypothetical system errors | 1 |
word sense disambiguation ( wsd ) is the task to identify the intended sense of a word in a computational manner based on the context in which it appears ( cite-p-13-3-4 )---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 | 1 |
we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit---we draw the conclusions of our study and describe our plans for extending the method | 0 |
mikolov et al proposed the word2vec method for learning continuous vector representations of words from large text datasets---mikolov et al propose word2vec where continuous vector representations of words are trained through continuous bag-of-words and skip-gram models | 1 |
the models we use are based on the generative dependency model with valence---the dependency model with valence is one of representative work , in which the valence is explicitly modelled | 1 |
in this paper we propose to use hawkes processes for classifying sequences of temporal textual data , which exploit both temporal and textual information---in this paper , we propose to use hawkes processes ( cite-p-12-1-2 ) , commonly used for modelling information | 1 |
the model parameters of word embedding are initialized using word2vec---we use the word2vec tool to pre-train the word embeddings | 1 |
also in the cross-language spirit , snyder and barzilay used cross-language mappings to learn morpheme patterns and consequently automatically segment words---snyder and barzilay proposed simultaneous morphology learning for discovery of abstract morphemes using multiple languages | 1 |
they later proposed a supervised approach for identifying whether a given sentence is prevalently msa or egyptian using the arabic online commentary dataset---elfardy and diab proposed a supervised method for identifying whether a given sentence in prevalently msa or egyptian using the arabic online commentary dataset | 1 |
we perform a systematic comparison of alternative compositional structures for constructing informative context representations---we perform a systematic comparison of alternative compositional architectures and propose a framework for error detection | 1 |
there has been a considerable amount of work on arabic morphological analysis---much work has been done on arabic computational morphology | 1 |
izumi et al proposed a maximum entropy model , using lexical and pos features , to recognize a variety of errors , including verb form errors---izumi et al train a maximum entropy model on error-tagged data from the japanese learners of english corpus , to detect 8 error types in the same corpus | 1 |
conditional random fields are undirected graphical models that are conditionally trained---barzilay and elhadad proposed lexical chains as an intermediate step in the text summarization process | 0 |
we use glove vectors with 200 dimensions as pre-trained word embeddings , which are tuned during training---we trained a 5-grams language model by the srilm toolkit | 0 |
the weights of the different feature functions were optimised by means of minimum error rate training---the parameter weights are optimized with minimum error rate training | 1 |
the weights of the different feature functions were optimised by means of minimum error rate training---the parameters of the systems were tuned using mert to optimize bleu on the development set | 1 |
this single endto-end nmt model outperforms the best conventional smt system ( cite-p-20-1-5 ) and achieves a state-of-the-art performance---these paraphrases can then be used for generating high precision surface patterns for relation extraction | 0 |
combinatory categorial grammars are a linguistically-motivated model for a wide range of language phenomena---target language models were trained on the english side of the training corpus using the srilm toolkit | 0 |
all language models are created with the srilm toolkit and are standard 4-gram lms with interpolated modified kneser-ney smoothing---ma et al used multiple sets of attentions , one for modeling the attention of aspect words and one for modeling the attention of context words | 0 |
by taking a structured prediction approach , we provide a large-margin method that directly optimizes a convex relaxation of the desired performance measure---as training examples , we formulate the learning problem as a structured prediction problem and derive a maximum-margin algorithm | 1 |
one of such tasks is the automatic boundary detection of predicate arguments of the kind defined in propbank---an interesting application of the sst kernel is the classification of the predicate argument structures defined in propbank or framenet | 1 |
we used the svm implementation provided within scikit-learn---we use the skll and scikit-learn toolkits | 1 |
for both languages , we used the srilm toolkit to train a 5-gram language model using all monolingual data provided---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 |
we used the srilm toolkit to generate the scores with no smoothing---a popular statistical machine translation paradigms is the phrase-based model | 0 |
in this research , we use the pre-trained google news dataset 2 by word2vec algorithms---we train the cbow model with default hyperparameters in word2vec | 1 |
we show that while sentiment features are useful for stance classification , they alone are not sufficient---even though sentiment features are useful for stance detection , they alone are not sufficient | 1 |
we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit---we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting | 1 |
specifically , we tested the methods word2vec using the gensim word2vec package and pretrained glove word embeddings---in this paper , we present an approach for extracting the named entities of natural language inputs which uses the maximum entropy framework | 0 |
we adopt glove vectors as the initial setting of word embeddings v---we use the glove algorithm to obtain 300-dimensional word embeddings from a union of these corpora | 1 |
therefore , in our sts system , we use a knowledge-based method to compute word similarity---in our work , we adopt a knowledge-based word similarity method with wsd to measure the semantic similarity between two sentences | 1 |
the code and data used in the experiments in this paper are available at http : //rtw.ml.cmu.edu/emnlp2015 sfe/---in this paper are available at http : / / rtw . ml . cmu . edu / emnlp2015 | 1 |
this idea is applied to the discovery of thematic interrelationships among the suras ( chapters ) of the qur¡¯an by abstracting lexical frequency data from them and then applying hierarchical cluster analysis to that data---preliminary results indicate that construction and semantic interpretation of cluster trees based on lexical frequency is a useful approach to discovering thematic interrelationships among the suras that constitute the qur ¡¯ an | 1 |
word alignment is the task of identifying translational relations between words in parallel corpora , in which a word at one language is usually translated into several words at the other language ( fertility model ) ( cite-p-18-1-0 )---word alignment is a natural language processing task that aims to specify the correspondence between words in two languages ( cite-p-19-1-0 ) | 1 |
trigram language models were estimated using the sri language modeling toolkit with modified kneser-ney smoothing---language models were built using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
we use the world atlas of language structure dataset , on which we conduct experiments---the dataset we used in the present study is the online edition 2 of the world atlas of language structures | 1 |
in this paper we addressed the problem of recommending questions from large archives of community question answering data based on users¡¯ information needs---in this paper we address the problem of question recommendation from large archives of community question answering data | 1 |
however , as shown in the experiments in , the whole sentiment of a document is not necessarily the sum of its parts---however , in , as the difficulty shown in the experiments , the whole sentiment of a document is not necessarily the sum of its parts | 1 |
social media is a popular public platform for communicating , sharing information and expressing opinions---in social media especially , there is a large diversity in terms of both the topic and language , necessitating the modeling of multiple languages simultaneously | 1 |
in this work , we study the possibility to construct sports news in the form of match reports from given live text commentary scripts---in this work , we treat the task of constructing sports news from live text commentary as a special kind of document | 1 |
marton et al use a similar set of morphological features to improve parsing accuracy for catib---marton et al use maltparser for parsing the catib treebank , and experiment with different combinations of rich morphological features | 1 |
wan translated both the training data and the test data to train different models in both the source and target languages---wan translates both the training data and the test data to train different models in both the source and target languages | 1 |
several wide-coverage statistical parsers have recently been developed for combinatory categorial grammar and applied to the wsj penn treebank---experiments on deep parsing of penn treebank have been reported for combinatory categorial grammar and lexical functional grammar | 1 |
in this paper , we present the benefits and feasibility of applying dependency structure in text-level discourse parsing---in this paper , we propose to adopt the dependency structure in discourse representation | 1 |
garrette et al describe an approach to combining logical semantics with distributional semantics using markov logic networks---we use 5-grams for all language models implemented using the srilm toolkit | 0 |
we use sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus---we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model | 1 |
the system dictionary of the mix-wp identifier is comprised of the ckip lexicon and those unknown words found automatically from the udn 2001 corpus by a chinese word autoconfirmation system---the system dictionary of our word-pair identifier is comprised of 155,746 chinese words taken from the moe-mandarin dictionary and 29,408 unknown words auto-found in udn2001 corpus by a chinese word autoconfirmation system | 1 |
we evaluate our approach and show that our models are able to outperform baselines on both the local and global level of frame knowledge---we demonstrate that our models are able to outperform baselines , thus indicating our ability to jointly model the local and global level information of predicate-argument structure | 1 |
word ng ) following , bykh and meurers , we used all word-based n-grams occurring in at least two texts of the training set---ocpos ng ) all ocpos n-grams occurring in at least two texts of the training set were obtained as described in bykh and meurers | 1 |
rothe and sch眉tze , 2015 ) also utilizes an autoencoder to jointly learn word , sense and synset representations in the same semantic space---rothe and sch眉tze , 2015 ) build a neural-network post-processing system called autoextend that takes word embeddings and learns embeddings for synsets and lexemes | 1 |
collobert et al proposed cnn architecture that can be applied to various nlp tasks , such as pos tagging , chunking , named entity recognition and semantic role labeling---in particular , collobert et al and turian et al learn word embeddings to improve the performance of in-domain pos tagging , named entity recognition , chunking and semantic role labelling | 1 |
in this paper , we proposed a feedback cleaning method that utilizes automatic evaluation to remove incorrect/redundant translation rules---to this problem , we propose a feedback cleaning method using automatic evaluation of mt quality , which removes incorrect / redundant rules | 1 |
this paper studies and evaluates the effects of language dynamics in the capitalization of newspaper corpora---this paper studies the impact of written language variations and the way it affects the capitalization task | 1 |
in the field of translation studies , it is undisputed that discourse-wide context must be considered carefully for good translation results ( cite-p-19-3-13 )---in the field of translation studies , it is undisputed that discourse-wide context must be considered carefully | 1 |
the first group is the translation models , which leverage the question-answer pairs to learn the semantically related words to improve traditional ir models ( cite-p-16-1-9 , cite-p-16-3-13 , cite-p-16-3-18 )---the first group consists of researches to construct a new translation dictionary for a fresh language pair from existing translation dictionaries or other language resources ( cite-p-9-1-3 ) | 1 |
we evaluated annotation reliability by using the kappa statistic---we used kappa statistics to evaluate the annotations made by the annotators in the second phase | 1 |
the language model is a 5-gram with interpolation and kneser-ney smoothing---twitter is a microblogging site where people express themselves and react to content in real-time | 0 |
we take this one step further and examine techniques that do not involve neural networks---givan ( 1992 ) introduce a syntax for first order logic which they call montagovian syntax | 0 |
semantic role labeling ( srl ) is the task of identifying semantic arguments of predicates in text---semantic role labeling ( srl ) is the task of labeling predicate-argument structure in sentences with shallow semantic information | 1 |
phrase-based models treat phrase as the basic translation unit---in this paper , we propose the method to combine the interactive disambiguation and the automatic one | 0 |
in this paper , we present a unified model for word sense representation and disambiguation that uses one representation per sense---in this paper , we have proposed a unified method for word sense representation and disambiguation | 1 |
in this paper , we propose a probabilistic model to explain speakers¡¯ choices of referring expressions based on discourse salience---in this domain can be derived from our speaker model , providing an explanation from first principles for the relation between discourse salience and speakers ¡¯ choices of referring expressions | 1 |
table 5 shows the bleu and per scores obtained by each system---table 3 shows results in terms of meteor and bleu | 1 |
dependency parsing is a way of structurally analyzing a sentence from the viewpoint of modification---dependency parsing consists of finding the structure of a sentence as expressed by a set of directed links ( dependencies ) between words | 1 |
sentence fusion is the technique of merging several input sentences into one output sentence while retaining the important content ( cite-p-22-1-0 , cite-p-22-1-6 , cite-p-22-3-10 )---sentence fusion is a text-to-text generation application , which given two related sentences , outputs a single sentence expressing the information shared by the two input sentences ( cite-p-6-1-0 ) | 1 |
while we focus on resolving deictic non-anaphoric zero pronouns---we focus primarily on the resolution of deictic zero pronouns | 1 |
comparisons with the state-of-the-art models show that our system produces better performance---following , we use the shift-reduce style algorithm to efficiently encode the word aligned phrase-pair as a normalized decomposition tree | 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---for the n-gram lm , we use srilm toolkits to train a 4-gram lm on the xinhua portion of the gigaword corpus | 1 |
averaging unreliable scores does not result in a reliable one---distributed word representations induced through deep neural networks have been shown to be useful in several natural language processing applications | 0 |
in this paper we present the system we submitted for the community question-answering task of the semeval 2016 workshop ,---in this paper , we aim to address the semeval 2016 tasks that are designed for answer selection and question retrieval | 1 |
we used a 5-gram language model with modified kneser-ney smoothing , built with the srilm toolkit---we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit | 1 |
we use a standard maximum entropy classifier implemented as part of mallet---for our al framework we decided to employ a maximum entropy classifier | 1 |
the models are implemented as support vector machine classifiers via the software package svm-light---this is done by training a multiclass support vector machine classifier implemented in the svmmulticlass package by joachims | 1 |
we used srilm to build a 4-gram language model with interpolated kneser-ney discounting---the targetside 4-gram language model was estimated using the srilm toolkit and modified kneser-ney discounting with interpolation | 1 |
lauer and subsequent studies demonstrate that the dependency model performs better than the adjacency model---in this paper , we test automatic annotation using conditional random fields which have achieved high performance for information extraction | 0 |
we also use a 4-gram language model trained using srilm with kneser-ney smoothing---the language model pis implemented as an n-gram model using the srilm-toolkit with kneser-ney smoothing | 1 |
following , we use the bootstrapresampling test to do significance testing---following , we use bootstrap resampling for significance testing | 1 |
therefore , we use double array trie structure for implementation---although there are many implementations for trie , we use a double-array in our task | 1 |
blei et al proposed lda as a general bayesian framework and gave a variational model for learning topics from data---for the first lstm model , we use softmax as our non-linear function and optimize the categorical cross entropy loss using adam | 0 |
for all classifiers , we used the scikit-learn implementation---we use the skll and scikit-learn toolkits | 1 |
this paper proposes a clustering-based stratified seed sampling approach to semi-supervised learning---coreference resolution is a key problem in natural language understanding that still escapes reliable solutions | 0 |
we trained a standard 5-gram language model with modified kneser-ney smoothing using the kenlm toolkit on 4 billion running words---we improved the system combination by adding a 5-grams language model with modified kneser-ney smoothing | 1 |
our implementation is available at https : //github.com/noahs-ark/spigot---at https : / / github . com / noahs-ark / spigot | 1 |
the weights of the different feature functions were optimised by means of minimum error rate training---we use the moses smt framework and the standard phrase-based mt feature set , including phrase and lexical translation probabilities and a lexicalized reordering model | 0 |
in this study , we focus on a new task of detecting community-related events via community emotion---we propose an event detection algorithm based on the sequence of community level emotion | 1 |
we present a novel approach to the task of word lemmatisation---in this paper , we present a new general approach to the task of lemmatisation | 1 |
conditional random fields , a statistical sequence modeling framework , was first introduced by lafferty et al---crfs , a statistical sequence modeling framework , was first introduced by lafferty et al | 1 |
bilingual lexicons play an important role in many natural language processing tasks , such as machine translation and cross-language information retrieval---bilingual dictionaries are an essential resource in many multilingual natural language processing tasks such as machine translation and cross-language information retrieval | 1 |
we evaluated the translation quality of the system using the bleu metric---we evaluate the translation quality using the case-sensitive bleu-4 metric | 1 |
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---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 |
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