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for our logistic regression classifier we use the implementation included in the scikit-learn toolkit 2---we use several classifiers including logistic regression , random forest and adaboost implemented in scikit-learn | 1 |
in this paper , we analyze the impact of syntactic structure on the sts 2014 and sick datasets of sts/sr tasks---in this paper , we make a preliminary evaluation of the impact of the syntactic structure in the tasks | 1 |
qian et al proposed a bilingual active learning paradigm for chinese and english relation classification with pseudo parallel corpora and entity alignment---a widely accepted way to use knowledge graph is tying queries with it by annotating entities in them , also known as entity linking | 0 |
recently , research and commercial communities have spent efforts to publish nlp services on the web---on the web , a lot of research effort is spent to aggregate the results of nlp tools | 1 |
a set of 500 sentences is used to tune the decoder parameters using the mert---the log-linear parameter weights are tuned with mert on a development set to produce the baseline system | 1 |
in addition to utilizing rule-based mt in st , this study used word graphs and chart parsing with new extensions---with the rule-based mt system , this study uses word graphs and chart parsing with new extensions | 1 |
neural machine translation has witnessed great successes in recent years---in recent years , neural machine translation has achieved great advancement | 1 |
as applications , the cross-lingually similarized grammars significantly improve the performance of dependency tree-based machine translation---as applications , the cross-lingually similarized grammars gain significant performance improvement for the dependency tree-based machine translation | 1 |
twitter is a widely used microblogging environment which serves as a medium to share opinions on various events and products---twitter is a subject of interest among researchers in behavioral studies investigating how people react to different events , topics , etc. , as well as among users hoping to forge stronger and more meaningful connections with their audience through social media | 1 |
sagan is a semantic textual similarity metric based on a complex textual entailment pipeline---the semantic textual similarity metric sagan is based on a complex textual entailment pipeline | 1 |
these features consist of parser dependencies obtained from the stanford dependency parser for the context of the target word---the grammatical relations are all the collapsed dependencies produced by the stanford dependency parser | 1 |
in this paper , we describe what we believe is a first attempt at building a multimodal system that detects deception in real-life settings---in this paper , we describe what we believe is a first attempt at building a multimodal system that detects deception | 1 |
we can use an automatic evaluation measure such as bleu as ev---we use bleu as the metric to evaluate the systems | 1 |
qiu et al propose a double propagation method to extract opinion word and opinion target simultaneously---concretely , qiu et al proposed a rulebased semi-supervised framework called double propagation for jointly extracting opinion words and targets | 1 |
as a proof of concept , we demonstrate the application of our graph for arithmetic question-answering---as a proof of concept , we demonstrate how our knowledge graph can be used to solve complex questions | 1 |
we evaluate the translation quality using the case-insensitive bleu-4 metric---for evaluation , we used the case-insensitive bleu metric with a single reference | 1 |
we use the conditional random fields learning algorithm in order to annotate the words with biesto labels---we used the moses toolkit with its default settings to build three phrase-based translation systems | 0 |
we integrated the transliteration extraction module into the giza++ word aligner and showed gains in alignment quality---it is found that each of the english equivalent synsets occurs in each separate class of english verbnet | 0 |
mikolov et al presents a neural network-based architecture which learns a word representation by learning to predict its context words---mikolov et al proposed a novel neural network model to train continuous vector representation for words | 1 |
cui et al developed a dependency-tree based information discrepancy measure---cui et al developed an information theoretic measure based on dependency trees | 1 |
we use the selectfrommodel 4 feature selection method as implemented in scikit-learn---we use the scikit-learn machine learning library to implement the entire pipeline | 1 |
segmentation is a nontrivial task in japanese because it does not delimit words by whitespace---once again , segmentation is the part of the process where the automatic algorithms most seriously underperform | 1 |
the data consists of sections of the wall street journal part of the penn treebank , with information on predicate-argument structures extracted from the propbank corpus---in other cases , these modules are integrated by means of statistical or uncertainty reasoning techniques | 0 |
yih et al use an array of lexical semantic similarity resources , from which they derive features for a binary classifier---wordnet is a key lexical resource for natural language applications | 0 |
each source of information is represented by a specific kernel function---sources of information are represented by kernel functions | 1 |
we perform minimum-error-rate training to tune the feature weights of the translation model to maximize the bleu score on development set---we adapt the minimum error rate training algorithm to estimate parameters for each member model in co-decoding | 1 |
we trained an english 5-gram language model using kenlm---we used the kenlm language model toolkit with character 7-grams | 1 |
we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing---for this language model , we built a trigram language model with kneser-ney smoothing using srilm from the same automatically segmented corpus | 1 |
we use the word2vec framework in the gensim implementation to generate the embedding spaces---during evaluation , we employ rouge as our evaluation metric | 0 |
incometo select the most fluent path , we train a 5-gram language model with the srilm toolkit on the english gigaword corpus---second , we present a unified approach to these problems | 0 |
the sentiment analysis is a field of study that investigates feelings present in texts---all parameters are initialized using glorot initialization | 0 |
this means in practice that the language model was trained using the srilm toolkit---the language models are 4-grams with modified kneser-ney smoothing which have been trained with the srilm toolkit | 1 |
furthermore , we also evaluate the method on alternate extrinsic loss functions---and show that the algorithm can incorporate additional loss functions | 1 |
the representative systems include medlee , metamap , knowledgemap , ctakes , etc---these systems include metamap , hi-tex , knowledgemap , medlee , symtext and mplus | 1 |
with word embeddings , each word is linked to a vector representation in a way that captures semantic relationships---thanks to the emergence of distributed representations of words , words are transformed to vectors that capture precise semantic word relationships | 1 |
by introducing a knowledge-based criterion , these new tags are decided whether or not to split into subcategories from a semantic perspective---secondly , a knowledge-based criterion is used to supervise the hierarchical splitting of these semantic-related tags | 1 |
koo et al and suzuki et al use unsupervised wordclusters as features in a dependency parser to get lexical dependencies---in order to reduce the amount of annotated data to train a dependency parser , koo et al used word clusters computed from unlabelled data as features for training a parser | 1 |
negation cue is a word , part of a word , or a combination of words that carries the negation information---a negation cue is a word , a phrase , a prefix , or a postfix that triggers negation | 1 |
we implement the pbsmt system with the moses toolkit---the lstm were introduced by hochreiter and schmidhuber and were explicitly designed to avoid the longterm dependency problem | 0 |
for word-level embedding e w , we utilize pre-trained , 300-dimensional embedding vectors from glove 6b---for the neural models , we use 100-dimensional glove embeddings , pre-trained on wikipedia and gigaword | 1 |
in the next section , we will experimentally verify svmv 's superiority---in section 4 , we verify our model ' s superiority over the others | 1 |
for this step we used regular expressions and nltk to tokenize the text---in this work , we use the path distance similarity measure provided in nltk | 1 |
steedman et al utilized a co-training parser for adaptation and showed that co-training is effective even across domains---steedman et al directly compare co-training and selftraining and find that co-training outperforms selftraining | 1 |
finally , in line with the finding in , hundreds of seeds are needed for tm to generalize---for tm in particular , hundreds of seeds are needed for generalization , in line with the finding in | 1 |
we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus---and all instances in both languages are then fed into a bilingual active learning engine | 0 |
the evaluation metric for the overall translation quality is caseinsensitive bleu4---the translation quality is evaluated by case-insensitive bleu and ter metric | 1 |
datasets we test our dependency model on 14 languages , including the english dataset from conll 2008 shared tasks and all 13 datasets from conll 2006 shared tasks---datasets we evaluate our model on standard benchmark corpora -conll 2006 and conll 2008 -which include dependency treebanks for 14 different languages | 1 |
topic modeling is a popular method for the task---topic modeling is a useful mechanism for discovering and characterizing various semantic concepts embedded in a collection of documents | 1 |
rapp proposed an approach to utilizing non-parallel corpora based on the assumption that the contexts of a term should be similar to the contexts of its translation in any language pairs---rapp utilized non-parallel corpora based on the assumption that the contexts of a term should be similar to the contexts of its translation in any language pairs | 1 |
we furthermore attempt to encourage the learning of the desired feature representations by pre-training the model¡¯s weights on two corresponding subtasks , namely , anaphoricity detection and antecedent ranking of known anaphoric mentions---that attempts to learn distinct feature representations for anaphoricity detection and antecedent ranking , which we encourage by pre-training on a pair of corresponding subtasks | 1 |
incometo select the most fluent path , we train a 5-gram language model with the srilm toolkit on the english gigaword 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 |
mihalcea et al developed several corpus-based and knowledge-based word similarity measures and applied them to a paraphrase recognition task---mihalcea et al used various text based similarity measures , including wordnet and corpus based similarity methods , to determine if two phrases are paraphrases | 1 |
similarly , work by councill et al showed that the accuracy of scope detection could be increased using the features from the dependency parse tree---finally , previous work reported that usage of syntactic dependency information helps in scope detection | 1 |
as embedding vectors , we used the publicly available representations obtained from the word2vec cbow model---based on word2vec , we obtained both representations using the skipgram architecture with negative sampling | 1 |
sentiment analysis is a natural language processing task whose aim is to classify documents according to the opinion ( polarity ) they express on a given subject ( cite-p-13-8-14 )---to keep consistent , we initialize the embedding weight with pre-trained word embeddings | 0 |
we used standard classifiers available in scikit-learn package---for nb and svm , we used their implementation available in scikit-learn | 1 |
we use the moses statistical mt toolkit to perform the translation---we use the moses smt toolkit to test the augmented datasets | 1 |
relation extraction is a core task in information extraction and natural language understanding---ccg , steedman , 1996 , steedman , 2000 is a linguistic formalism that tightly couples syntax and semantics , and can be used to model a wide range of language phenomena | 0 |
we use the stanford parser for obtaining all syntactic information---for english , we use the stanford parser for both pos tagging and cfg parsing | 1 |
we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit from stolcke | 1 |
semantic parsing is the task of mapping natural language to machine interpretable meaning representations---semantic parsing is the task of mapping natural language to a formal meaning representation | 1 |
the results show that the proposed adaptation recipe improves not only the objective scores but also the user¡¯s perceived quality of the system---evaluation results show that the proposed procedure can achieve competitive performance in terms of bleu score and slot error rate | 1 |
we also adopt dropout upon the output layer of cnn---we add dropout layers to the input and output of the rnn | 1 |
named entity recognition ( ner ) is the task of identifying named entities in free text—typically personal names , organizations , gene-protein entities , and so on---named entity recognition ( ner ) is the task of detecting named entity mentions in text and assigning them to their corresponding type | 1 |
moreover , we find that jointly learning ‘ natural ’ subtasks , in a multi-task learning setup , improves performance---we show that a multi-task learning setup where natural subtasks of the full am problem are added as auxiliary tasks improves performance | 1 |
among them , twitter is the most popular service by far due to its ease for real-time sharing of information---twitter is a very popular micro blogging site | 1 |
the smt systems were built using the moses toolkit---it was trained on the webnlg dataset using the moses toolkit | 1 |
further , we apply a 4-gram language model trained with the srilm toolkit on the target side of the training corpus---we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing | 1 |
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---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 |
we used the moses toolkit for performing statistical machine translation---we ran mt experiments using the moses phrase-based translation system | 1 |
the integrated dialect classifier is a maximum entropy model that we train using the liblinear toolkit---for the ¡° complete ¡± model , we checked the top 20 answer candidates that ranked higher than the actual ¡° correct ¡± | 0 |
we treat this subset of keywords as a sequence and propose a sequence to sequence model using rnn to generate a natural language question from it---the nnlm weights are optimized as the other feature weights using minimum error rate training | 0 |
cite-p-19-3-19 , cite-p-19-3-20 showed through similar analyses of emotion words that the three primary independent dimensions of emotions are valence or pleasure ( positiveness– negativeness/pleasure–displeasure ) , arousal ( active–passive ) , and dominance ( dominant– submissive )---studies have shown that the three most important , largely independent , dimensions of word meaning are valence ( positiveness – negativeness / pleasure – displeasure ) , arousal ( active – passive ) , and dominance ( dominant – submissive ) ( cite-p-19-3-15 , cite-p-19-3-19 , cite-p-19-3-20 ) | 1 |
relation extraction is a crucial task in the field of natural language processing ( nlp )---relation extraction ( re ) is the task of recognizing the assertion of a particular relationship between two or more entities in text | 1 |
word sense disambiguation ( wsd ) is the nlp task that consists in selecting the correct sense of a polysemous word in a given context---in natural language , a word often assumes different meanings , and the task of determining the correct meaning , or sense , of a word in different contexts is known as word sense disambiguation ( wsd ) | 1 |
fasttext pre-trained vector is used for word embedding with embed size is 300---for our baseline we use the moses software to train a phrase based machine translation model | 0 |
semantic role labeling ( srl ) is the process of producing such a markup---semantic role labeling ( srl ) is the task of labeling the predicate-argument structures of sentences with semantic frames and their roles ( cite-p-18-1-2 , cite-p-18-1-19 ) | 1 |
relation extraction ( re ) is the process of generating structured relation knowledge from unstructured natural language texts---relation extraction ( re ) is the task of assigning a semantic relationship between a pair of arguments | 1 |
since coreference resolution is a pervasive discourse phenomenon causing performance impediments in current ie systems , we considered a corpus of aligned english and romanian texts to identify coreferring expressions---coreference resolution is the process of finding discourse entities ( markables ) referring to the same real-world entity or concept | 1 |
we use a pbsmt model where the language model is a 5-gram lm with modified kneser-ney smoothing---the language model is a large interpolated 5-gram lm with modified kneser-ney smoothing | 1 |
for example , the rhetorical structure theory represents a discourse as a tree with phrases or clauses as elementary discourse units---for example , rhetorical structure theory defines 23 types of discourse relations that are used to structure the text into complex discourse trees | 1 |
we preprocess the data using the clearnlp segmenter 2 via dkpro core---we extract the features using tools for natural language processing provided by dkpro core | 1 |
moreover , we augment our model with the attention mechanism to push the model to distill the relevant information from context---based on their semantic relatedness , our system incorporates some lexical and syntactic similarity measures to make the system robust | 0 |
in order to perform an exhaustive comparison , we also implemented two rule-based and two baseline sentence-planners---in order to perform an exhaustive comparison , we also evaluate a hand-crafted template-based generation component , two rule-based sentence | 1 |
shen et al proposed a string-to-dependency model , which restricted the target-side of a rule by dependency structures---shen et al describe the result of filtering rules by insisting that target-side rules are well-formed dependency trees | 1 |
using the knowledge bases , we develop an inference mechanism to recognize and explain the metaphors in the text---we combine the two knowledge bases and a probabilistic reasoning mechanism for automatic metaphor recognition and explanation | 1 |
in this paper , we propose a novel cognition based attention model to improve the state-of-the-art neural sentiment analysis model through cognition grounded eye-tracking data---the weights 位 m in the log-linear model were trained using minimum error rate training with the news 2009 development set | 0 |
relation extraction ( re ) is the task of recognizing the assertion of a particular relationship between two or more entities in text---relation extraction ( re ) is the task of recognizing relationships between entities mentioned in text | 1 |
interestingly , as reported in , a simple averaging scheme was found to be very competitive to more complex models for high level semantic tasks despite its simplicity---as reported in , a simple averaging scheme was found to be very competitive to more complex models for representing a sentence vector | 1 |
discourse segmentation is the first step when building a discourse parser , and has a large impact on the building of the final structure – predicted segmentation leads to a drop in performance of about 12-14 % ( cite-p-21-1-16 )---discourse segmentation is a crucial step in building endto-end discourse parsers | 1 |
fortunately , nivre et al propose a constrained decoding procedure for the arc-eager parsing system---we use the pre-trained glove 50-dimensional word embeddings to represent words found in the glove dataset | 0 |
in our experiment , word embeddings were 200-dimensional as used in , trained on gigaword with word2vec---we used 300 dimensional skip-gram word embeddings pre-trained on pubmed | 1 |
we use the arc-based features of turboparser , which descend from several other feature models from the literature on syntactic dependency parsing---we used the scikit-learn implementation of svrs and the skll toolkit | 0 |
cnns have proven useful for various nlp tasks because of their effectiveness in identifying patterns in their input---coreference resolution is a well known clustering task in natural language processing | 0 |
automatic evaluation measures are used in evaluating simulated dialog corpora---evaluation measures are sufficient to discern simulated from real dialogs | 1 |
we describe a state-of-the-art automatic system that can acquire subcategorisation frames from raw text for a free word-order language---we have presented a state-of-the-art subcategorisation acquisition system for free-word order languages , and used it to create a large subcategorisation frame | 1 |
it is worth noting that the morpheme feature is employed to better represent the compositional semantics inside chinese words---following this , hoffmann et al and surdeanu et al propose models that consider the mapping as that of multi-instance multi-label learning | 0 |
our experiments show that this approach outperforms competitive algorithms on several datasets tested---we applied our annotation scheme to the product review dataset 4 released by hu and liu | 0 |
in this paper , we present a method which linearizes amr graphs in a way that captures the interaction of concepts and relations---sketch engine has been widely deployed in lexicography and the study of language learning , but less often for broader questions in social science | 0 |
in this paper , we propose using web search clickthrough logs to learn semantic categories---our proposed method employs search clickthrough logs to improve semantic category acquisition | 1 |
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