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the generation of referring expressions is a core ingredient of most natural language generation systems---we applied liblinear via its scikitlearn python interface to train the logistic regression model with l2 regularization | 0 |
the core part of our algorithm is a cognitively-motivated scheduler according to which training instances and their “ reviews ” are spaced over time---core part of our algorithm is a scheduler that ensures a given neural network spends more time working on difficult training instances | 1 |
the results reported here indicate that the proposed methodology yields usable results in understanding the qur¡¯an on the basis of its lexical semantics---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 |
predicates in the data are typically verbs , biomedical text often prefers nominalizations , gerunds and relational nouns---predicates in newswire text are typically verbs , biomedical text often prefers nominalizations , gerunds , and relational nouns | 1 |
distributional semantic models are employed to produce semantic representations of words from co-occurrence patterns in texts or documents---distributional semantic models induce large-scale vector-based lexical semantic representations from statistical patterns of word usage | 1 |
according to experimental results , we suggest a prediction solution by considering max-confidence as the upper bound and min-error as the lower bound for stopping conditions---according to our experimental results , we suggest a prediction solution by considering max-confidence as the upper bound and min-error as the lower bound of stopping conditions | 1 |
in this work we explore the use of skip-thought vectors to create distributed representations that encode features that are predictive with respect to idiom token classification---collobert et al adjust the feature embeddings according to the specific task in a deep neural network architecture | 0 |
chinese is a language that does not have morphological tense markers that provide explicit grammaticalization of the temporal location of situations ( events or states )---content of the training set , our approach preferentially samples high perplexity sentences , as determined by an easily queryable n-gram language model | 0 |
this raises the question as to which is a more accurate characterisation of what people do---this raises the question of what is the difference between syntactic approaches generally and semantic approaches generally | 1 |
semantic role labeling ( srl ) is the task of automatically labeling predicates and arguments in a sentence with shallow semantic labels---semantic role labeling ( srl ) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence | 1 |
sentiment analysis is a growing research field , especially on web social networks---sentiment analysis is a technique to classify documents based on the polarity of opinion expressed by the author of the document ( cite-p-16-1-13 ) | 1 |
significance tests are conducted using bootstrap sampling---the statistical significance test is performed by the re-sampling approach | 1 |
the pos tags used in the reordering model are obtained using the treetagger---word-overlap baseline , it has the advantage of taking into account the distributional similarity between words that are also involved in compositional models | 0 |
we use the penn discourse treebank , which is the largest handannotated discourse relation corpus annotated on 2312 wall street journal articles---in our work we use the penn discourse treebank , the largest public resource containing discourse annotations | 1 |
more recently , discriminatively-trained models have been shown to be more accurate than generative models---it has been argued that locally trained algorithms can suffer from label bias issues | 1 |
this approach has showed significant and consistent improvements when applied to automatic speech recognition and machine translation tasks---this approach was successfully used in large vocabulary continuous speech recognition and in a phrase-based system for a small task | 1 |
in the experiments , we train a fasttext model over the english wikipedia corpus to generate term embeddings---we combine all the unaligned monolingual source and target sentences on all five domains to train a skip-gram model using fasttext | 1 |
mutalik et al developed negfinder , a rulebased system that recognises negated patterns in medical documents---mutalik et al developed another rule based system called negfinder that recognizes negation patterns in biomedical text | 1 |
hu et al , 2016 , explored a distillation framework that transfers structured knowledge coded as logic rules into the weights of neural networks---for japanese-to-english task , we use a chunkbased japanese dependency tree | 0 |
a trigram language model with modified kneser-ney discounting and interpolation was used as produced by the srilm toolkit---we use pre-trained glove vector for initialization of word embeddings | 0 |
we extend this idea so that we can change the output length flexibly---however , we need to modify this model to appropriately process more complicated sentences | 1 |
models are evaluated in terms of bleu , meteor and ter on tokenized , cased test data---the performance of the different systems is evaluated in terms of translation error rate , bleu , and precision | 1 |
second , twsss share common structure with sentences in the erotic domain---and ( 2 ) twsss share common structure with sentences in the erotic domain | 1 |
linear svm classifiers are a highly robust supervised classification method that has proven to be very effective for text classification---svms have proven to be an effective means for text categorization as they are capable to robustly deal with high-dimensional , sparse feature spaces | 1 |
in table 3 we examine , using an analysis similar to that in durrett and klein , where the unpipelined models go wrong---in table 6 we show a more fine-grained breakdown inspired by a similar analysis in durrett and klein | 1 |
the research area that deals with the computational treatment of opinion , sentiment and subjectivity in texts is called sentiment analysis ( cite-p-12-1-7 )---sentiment analysis ( cite-p-8-1-20 ) is a task of predicting whether the text expresses a positive , negative , or neutral opinion in general or with respect to an entity of interest | 1 |
we performed significance testing using paired bootstrap resampling---in this paper , we improve domain-specific word alignment through statistical alignment | 0 |
roth and yih have combined named entity recognition and relation extraction in a structured prediction approach to improve both tasks---compressing deep models into smaller networks has been an active area of research | 0 |
the main idea is to learn a high-level abstract representation that is discriminative for the main classification task , but is invariant across the input languages---the network described so far learns the abstract features through multiple hidden layers that are discriminative for the classification task | 1 |
relation extraction is a challenging task in natural language processing---markable detection ) and domain ; and is able to deliver good results for shallow information spaces and competitive results for rich feature spaces | 0 |
the weights used during the reranking are tuned using the minimum error rate training algorithm---however , such a model is too generic and does not exploit the specific characteristics of this task | 0 |
we tackle the concept normalisation task in a different manner---in this work , we handle the medical concept normalisation | 1 |
we used the moses toolkit to extract a scfg following chiang from the 6 th version of the europarl collection---we used the sentence-aligned europarl corpus for the construction of our wsd module | 1 |
named entity recognition ( ner ) is the process by which named entities are identified and classified in an open-domain text---named entity recognition ( ner ) is the first step for many tasks in the fields of natural language processing and information retrieval | 1 |
in query-focused summarization , the task is to produce a summary as an answer to a given query---the examples of convolution methods being successfully used in nlp are kernels based on dependency trees and shallow parsing | 0 |
for probabilities , we trained 5-gram language models using srilm---we used srilm to build a 4-gram language model with kneser-ney discounting | 0 |
word alignment is a well-studied problem in natural language computing---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 |
in ( cite-p-19-1-6 ) , first-order rewrite rule feature spaces have been explored---the first-order rule feature space , introduced by ( cite-p-19-3-10 ) , gives high performances in term of accuracy | 1 |
consequently , such segmenters can not produce consistently good results when used across different domains---segmenters in order to improve the robustness of segmenters across different domains | 1 |
semantic parsing is the problem of translating human language into computer language , and therefore is at the heart of natural language understanding---semantic parsing is the task of mapping natural language sentences to a formal representation of meaning | 1 |
more recently , cue phrases have been applied to topic segmentation in the supervised setting---addition of cue phrases can further improve segmentation performance over cohesion-based methods | 1 |
baroni et al use similar statistics to help discover morphologically-related words---zelenko et al used the kernel methods for extracting relations from text | 0 |
the language model is a trigram model with modified kneser-ney discounting and interpolation---the srilm language modelling toolkit was used with interpolated kneser-ney discounting | 1 |
in this paper , we explore methods for restricting the space of possible tts templates under consideration , while still allowing good templates to emerge directly from the data as much as possible---and applying a set of constraints , we restrict the space of possible tts templates under consideration , while still allowing new and more accurate templates to emerge from the training data | 1 |
we use 5-grams for all language models implemented using the srilm toolkit---while natural language text is a rich source to obtain broad knowledge about the world , compiling trivial commonsense knowledge from unstructured text is a nontrivial feat | 0 |
the hierarchical phrase-based model has been widely adopted in statistical machine translation---synchronous context-free grammars are now widely used in statistical machine translation , with hiero as the preeminent example | 1 |
the model simulates language processing as a collective phenomenon that emerges from a myriad of microscopic and diverse activities---this paper proposes that the process of language understanding can be modeled as a collective phenomenon that emerges from a myriad of microscopic and diverse activities | 1 |
in this paper , we address target-dependent sentiment classification of tweets---in this paper , we propose to improve target-dependent sentiment classification of tweets | 1 |
a metaphor is a figure of speech that creates an analogical mapping between two conceptual domains so that the terminology of one ( source ) domain can be used to describe situations and objects in the other ( target ) domain---in this paper , we propose to model each document as a multivariate gaussian distribution | 0 |
frame induction is the automatic creation of frame-semantic resources similar to framenet or propbank , which map lexical units of a language to frame representations of each lexical unit ’ s semantics---coreference resolution is the process of finding discourse entities ( markables ) referring to the same real-world entity or concept | 0 |
the standard phrase-based machine translation system focuses on finding the most probable target sentence given the source sentence---phrase based systems rely on a lexicalized distortion model and the target language model to produce output words in the correct order | 1 |
so if we are serious about a sentential theory of attitudes , it is important to be certain that such a theory can explain opaque indexicals---but i will argue that the new theory explains the opacity of indexicals while maintaining the advantages of a sentential theory of attitudes | 1 |
in order to train the ranker , we adopt the ranking svm algorithm , which learns a weight vector to rank candidates for a given partial ranking of each discourse entity---although the work by denis and baldridge uses maximum entropy to create their ranking-based model , we adopt the ranking svm algorithm , which learns a weight vector to rank candidates for a given partial ranking of each referent | 1 |
coreference resolution is the task of determining which mentions in a text refer to the same entity---in this paper , we presented a new approach for domain adaptation | 0 |
relation extraction ( re ) has been defined as the task of identifying a given set of semantic binary relations in text---relation extraction is the problem of populating a target relation ( representing an entity-level relationship or attribute ) with facts extracted from natural-language text | 1 |
koehn and knight use similarity in spelling as another kind of cue that a pair of words may be translations of one another---koehn and knight used similarity in spelling as another kind of cue that a pair of words may be translations of one another | 1 |
in this work , we propose to use context gates to control the contributions of source and target contexts on the generation of target words ( decoding ) in nmt---based on this observation , we propose using context gates in nmt to dynamically control the contributions from the source and target contexts | 1 |
sentence compression is a paraphrasing task where the goal is to generate sentences shorter than given while preserving the essential content---sentence compression is a complex paraphrasing task with information loss involving substitution , deletion , insertion , and reordering operations | 1 |
for language model , we used sri language modeling toolkit to train a 4-gram model with modified kneser-ney smoothing---we built a 5-gram language model from it with the sri language modeling toolkit | 1 |
in a second baseline model , we also incorporate 300-dimensional glove word embeddings trained on wikipedia and the gigaword corpus---in this paper , we compare our technique with the grammar checker of microsoft word03 and the alek method used by ets | 0 |
an effective solution for these problems is the long short-term memory architecture---in this study , we propose a representation learning approach which simultaneously learns vector representations for the texts | 0 |
table 1 shows the performance for the test data measured by case sensitive bleu---automatic evaluation results are shown in table 1 , using bleu-4 | 1 |
in this paper , three subclasses of lfg 's called nc-lfg 's , dc-lfg 's and fc-lfg 's are proposed , two of which can be recognized in polynomial time---in this paper , three subclasses of lfg ' s called nc-lfg ' s , dc-lfg ' s and fc-lfg ' s are introduced | 1 |
we use semafor as a black box to obtain the semantic parse of a sentence---we use semafor for obtaining the semantic parse of a sentence | 1 |
sentiment analysis ( sa ) is the determination of the polarity of a piece of text ( positive , negative , neutral )---sentiment analysis ( sa ) is a fundamental problem aiming to allow machines to automatically extract subjectivity information from text ( cite-p-16-5-8 ) , whether at the sentence or the document level ( cite-p-16-3-3 ) | 1 |
wikipedia is a constantly evolving source of detailed information that could facilitate intelligent machines — if they are able to leverage its power---wikipedia is a free , collaboratively edited encyclopedia | 1 |
the weight parameter 位 is tuned by a minimum error-rate training algorithm---the weights for the loglinear model are learned using the mert system | 1 |
the conll 2008-2009 shared tasks introduced a variant where semantic dependencies are annotated rather than phrasal arguments---the word embeddings for semantic similarity computation are learned using the word2vec tool on a dataset consisting of 85,000 student essays collected from the web | 0 |
time normalization is the task of translating natural language expressions of time to computer-readable forms---time normalization is the task of converting a natural language expression of time into a formal representation of a time on a timeline | 1 |
we also trained 5-gram language models using kenlm---five-gram language models are trained using kenlm | 1 |
shift-reduce parsing for cfg and dependency parsing have recently been studied , through approaches based essentially on deterministic parsing---incremental deterministic classifier-based parsing algorithms have been studied in dependency parsing and cfg parsing | 1 |
translation performance is measured using the automatic bleu metric , on one reference translation---translation performances are measured with case-insensitive bleu4 score | 1 |
table 1 shows the translation performance by bleu---table 3 shows results in terms of meteor and bleu | 1 |
in this paper , we presented a novel corpus of comparable texts that provides full discourse contexts for alternative verbalizations---in this novel corpus , we identify common events across texts and investigate the argument structures that were realized in each context | 1 |
kalchbrenner et al developed a cnnbased model that can be used for sentence modelling problems---kalchbrenner et al showed that their dcnn for modeling sentences can achieve competitive results in this field | 1 |
cite-p-10-1-3 and cite-p-10-1-6 predict hierarchical power relations between people in the enron email corpus using lexical features extracted from all the messages exchanged between them---cite-p-10-1-3 and cite-p-10-1-6 built systems to predict hierarchical power relations between people in the enron email corpus using lexical features from all the messages exchanged between them | 1 |
relation extraction is a crucial task in the field of natural language processing ( nlp )---relation extraction ( re ) is the task of assigning a semantic relationship between a pair of arguments | 1 |
in this section , we first briefly review the conventional skip-gram model---here , we briefly review the approaches of learning distributed representations of words and documents | 1 |
traditional topic models like latent dirichlet allocation have been explored extensively to discover topics from text---traditional topic models such as lda and plsa are unsupervised methods for extracting latent topics in text documents | 1 |
irony detection is a key task for many natural language processing works---irony detection is a problem that is important for the working of many natural language understanding systems | 1 |
the sentence encoder can also be implemented with gru or lstm---the birnn is implemented with lstms for better long-term dependencies handling | 1 |
this study proves that fast and accurate ensemble parsers can be built with minimal effort---work proves that ensemble parsers that are both accurate and fast can be rapidly developed with minimal effort | 1 |
tag s denotes scene boundary , c denotes character mention , d denotes dialogue , n denotes scene description , and m denotes metadata---s denotes scene boundary , c denotes character mention , d denotes dialogue , n denotes scene description | 1 |
the annotation scheme is based on an evolution of stanford dependencies and google universal part-of-speech tags---jiang et al proposes a cascaded linear model for joint chinese word segmentation and pos tagging | 0 |
it is true that they have similar context and co-occurrence information when words are used with the same sense---it is generally true that when words are used in the same sense , they have similar context and co-occurrence information | 1 |
the hybrid approach integrates the rule-based approach with the ml-based approach in order to optimize the overall performance---on the penn chinese treebank 5 . 0 , it achieves an f-measure of 98 . 43 % , significantly outperforms previous works | 0 |
ent ) models implement the intuition that the best model will be the one that is consistent with the set of constrains imposed by the evidence , but otherwise is as uniform as possible---me models implement the intuition that the best model will be the one that is consistent with the set of constrains imposed by the evidence , but otherwise is as uniform as possible | 1 |
in this work , we propose a new approach to obtain temporal relations from time anchors , i.e . absolute time value , of all mentions---core part of our algorithm is a scheduler that ensures a given neural network spends more time working on difficult training instances | 0 |
all the data and the code to replicate the results given in this paper is available from the authors¡¯ website at http : //goo.gl/roqeh---wall street journal dataset , is available at the authors ¡¯ website at http : / / goo . gl / roqeh | 1 |
bharati et al illustrated that mere animacy of a nominal significantly improves the accuracy of the parser---bharati et al showed that a major chunk of errors in their parser is due to non-projectivity | 1 |
language models were estimated using the sri language modeling toolkit with modified kneser-ney smoothing---trigram language models were estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
in this paper , we presented a model based on latent semantics that is able to perform word sense induction as well as disambiguation---we adopt the idea of translation model entropy from koehn et al | 0 |
we extract syntactic dependencies using stanford parser and use its collapsed dependency format---second , these approaches are trained on the syntactic trees of the target language , which enables them to directly link the quality of newly induced categories | 0 |
the ordering within feature hierarchies has been the subject of investigation in work such as , and---the ordering within feature hierarchies has been the subject of investigation in work such as | 1 |
translating test sentences in target language into source language and inputting them into a source language system 2---translating a source language training corpus into target language and creating a corpusbased system in target language 3 | 1 |
transliteration is the conversion of a text from one script to another---the advent of the supervised method proposed by gildea and jurafsky has led to the creation of annotated corpora for semantic role labeling | 0 |
we used an l2-regularized l2-loss linear svm to learn the attribute predictions---we trained the l1-regularized logistic regression classifier implemented in liblinear | 1 |
according to semeval 2018 ’ s metrics , our model runs got final scores of 0.636 , 0.531 , 0.731 , 0.708 , and 0.408 in terms of pearson correlation on 5 subtasks , respectively---according to the metrics of semeval 2018 , our system gets the final scores of 0 . 636 , 0 . 531 , 0 . 731 , 0 . 708 , and 0 . 408 in terms of pearson correlation | 1 |
we use word2vec technique to compute the vector representation of all the tags---for efficiency , we follow the hierarchical softmax optimization used in word2vec | 1 |
this problem is called open ( world ) classification---a 4-grams language model is trained by the srilm toolkit | 0 |
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