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word information is used to process known words , and character information is used for unknown words in a similar way to ng and low---word information is used to process known-words , and character information is used for unknown words in a similar way to ng and low | 1 |
among these , the berkeley framenet database is a semantic lexical resource consisting of frame-semantic descriptions of more than 7000 english lexical items , together with example sentences annotated with semantic roles---the berkeley framenet database consists of frame-semantic descriptions of more than 7000 english... | 1 |
the log-linear feature weights are tuned with minimum error rate training on bleu---位 8 are tuned by minimum error rate training on the dev sets | 1 |
the ratio between true candidates and all candidates serves as a lower baseline , which is also called baseline precision---the ratio between true candidates and all candidates serves as lower baseline , which is also called baseline precision | 1 |
collobert et al apply generic neural network architectures to several sequence labelling tasks and obtain competitive results despite of the task-specific variations---collobert et al adjust the feature embeddings according to the specific task in a deep neural network architecture | 1 |
word embedding models are aimed at learning vector representations of word meaning---discriminative models are capable of incorporating domain knowledge , by adding diverse and overlapping features | 0 |
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---specifically , a metaphor is a mapping of concepts from a source domain to a target domain (... | 1 |
for the fst representation , we used the the opengrm-ngram language modeling toolkit and used an n-gram order of 4 , with kneser-ney smoothing---we achieve pitch accent and boundary tone accuracy of 85 . 2 % and 91 . 5 % on the same training and test sets used in ( cite-p-20-1-9 , cite-p-20-1-13 ) | 0 |
the language is a form of modal propositional logic---language is a weaker source of supervision for colorization than user clicks | 1 |
we chose the three models that achieved at least one best score in the closed tests from emerson , as well as the sub-word-based model of zhang , kikui , and sumita for comparison---we chose the three models that achieved at least one best score in the closed tests from emerson , as well as the sub-word-based model of ... | 1 |
this indicates that td children are adhering to a common target topic , while children with asd are introducing topic changes---similarity measures by themselves might indicate that children with asd are using semantically appropriate | 1 |
takamura et al used the spin model to extract word semantic orientation---methods for fine-grained sentiment analysis are developed by hu and liu , ding et al and popescu and etzioni | 0 |
pereira et al cluster nouns according to their distribution as direct objects of verbs , using information-theoretic tools---pereira et al use an information-theoretic based clustering approach , clustering nouns according to their distribution as direct objects among verbs | 1 |
in order to train the argument identification and role label disambiguation classifiers , we used the english portion of the conll 2009 shared task---coreference resolution is the task of partitioning the set of mentions of discourse referents in a text into classes ( or ‘ chains ’ ) corresponding to those referents ( ... | 0 |
word sense disambiguation ( wsd ) is the task of identifying the correct meaning of a word in context---for english , we use the stanford parser for both pos tagging and cfg parsing | 0 |
paradigmatic gaps present an interesting challenge for theories of inflectional structure and language learning---paradigmatic gaps are puzzling because they seemingly contradict the highly productive nature of inflectional systems | 1 |
we primarily compared our model with conditional random fields---in our experiments we use a publicly available implementation of conditional random fields | 1 |
neural networks , working on top of conventional n-gram back-off language models , have been introduced in as a potential means to improve discrete language models---neural networks , working on top of conventional n-gram models , have been introduced in as a potential means to improve conventional n-gram language mode... | 1 |
our system is based on the conditional random field---we use the mallet implementation of conditional random fields | 1 |
therefore , attempts have been made to develop unsupervised and knowledge based techniques for wsd which do not need sense marked corpora---unsupervised and knowledge based approaches have been tried with the hope of creating wsd systems with no need for sense marked corpora | 1 |
the translation results are evaluated by caseinsensitive bleu-4 metric---the translation results are evaluated with case insensitive 4-gram bleu | 1 |
long short-term memory was introduced by hochreiter and schmidhuber to overcome the issue of vanishing gradients in the vanilla recurrent neural networks---long short-term memory units , introduced by hochreiter and schmidhuber , are used in recurrent neural networks as a way to prevent vanishing or exploding gradients | 1 |
for chinese-english , we train a standard phrase-based smt system over the available 21,863 sentences---as in reichart and rappoport , we see large improvements when self-training on a small seed size without using the reranker | 0 |
soricut and echihabi propose documentlevel features to predict document-level quality for ranking purposes , having bleu as quality label---soricut and echihabi explore pseudoreferences for document-level qe prediction to rank outputs from an mt system | 1 |
as monolingual baselines , we use the skip-gram and cbow methods of mikolov et al as implemented in the gensim package---we train word embeddings using the continuous bag-of-words and skip-gram models described in mikolov et al as implemented in the open-source toolkit word2vec | 1 |
our main aim was not to build a complete model to handle all possible lm scenarios , but to present a “ proofof-concept ” study to test the potentialities of this approach---the standard classifiers are implemented with scikit-learn | 0 |
all the language models are built with the sri language modeling toolkit---trigram language models are implemented using the srilm toolkit | 1 |
dependency parsing is a crucial component of many natural language processing ( nlp ) systems for tasks such as relation extraction ( cite-p-15-1-5 ) , statistical machine translation ( cite-p-15-5-7 ) , text classification ( o ? zgu ? r and gu ? ngo ? r , 2010 ) , and question answering ( cite-p-15-3-0 )---to calculat... | 0 |
recent research in this area has resulted in the development of several large kgs , such as nell , yago , and freebase , among others---in recent years a variety of large knowledge bases have been constructed eg , freebase , dbpedia , nell , and yago | 1 |
the novelty of our approach lies in the feature generation and weighting , using not only single words and ngrams as features but also skipgrams---relation extraction ( re ) is the task of identifying instances of relations , such as nationality ( person , country ) or place of birth ( person , location ) , in passages... | 0 |
in this paper , we search across all possible tree structures whilst searching for the best word ordering---in this paper , we presented a new use of spanning tree algorithms for generating sentences | 1 |
coreference resolution is the process of linking multiple mentions that refer to the same entity---coreference resolution is the problem of identifying which noun phrases ( nps , or mentions ) refer to the same real-world entity in a text or dialogue | 1 |
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---sentiment analysis ( cite-p-12-3-17 ) is a popular research topic which has a wide range of applications , such as summarizing customer... | 1 |
we use liblinear logistic regression module to classify document-level embeddings---we experiment with linear kernel svm classifiers using liblinear | 1 |
first , we can incorporate features on phrase pairs , in addition to word links---our baseline is a standard phrase-based smt system | 0 |
we have shown in such a situation how mbr decoding can be applied to the mt system---compared to the mt based cross-lingual model , our model achieves a comparable and even better performance | 0 |
we use the features described in visweswariah et al that were based on features used in dependency parsing---we use the mstparser implementation described in mcdonald et al for feature extraction | 1 |
regarding to this , cite-p-20-3-11 explicitly feed this target word into the attention model , and demonstrate the significant improvements in alignment accuracy---as math-w-6-1-1-75 , we can see below that if the test statistics are independent | 0 |
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---coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world | 1 |
we trained individual logistic regression classifiers for the eight categories and the 50 top section types for each one using the default l2 regularization parameter in liblinear---the bleu metric has been widely accepted as an effective means to automatically evaluate the quality of machine translation outputs | 0 |
moreover , it allows us to incorporate a wide range of additional features---that enables us to incorporate a wide range of features | 1 |
word sense induction ( wsi ) is the task of automatically discovering all senses of an ambiguous word in a corpus---word sense induction ( wsi ) is the task of automatically identifying the senses of words in texts , without the need for handcrafted resources or manually annotated data | 1 |
by using the algorithm , each similarity between nodes is calculated , and the similarity matrix in figure 5 shows a similarity matrix s of v---neural networks ( rnns ) can also be used for language modeling | 0 |
second , implicit discourse relation is quite frequent in text---that is important for implicit discourse relation classification | 1 |
lda is a representative probabilistic topic model of document collections---taglda is a representative latent topic model by extending latent dirichlet allocation | 1 |
relation extraction is a fundamental task in information extraction---relation extraction is a fundamental task that enables a wide range of semantic applications from question answering ( cite-p-13-3-12 ) to fact checking ( cite-p-13-3-10 ) | 1 |
the proposed model can leverage gpus to reduce training time from three weeks for a state-of-the-art gbt classifier to three days while maintaining more than 96 % accuracy---classifier , the proposed model reduces training time from three weeks to three days while maintaining more than 96 % accuracy | 1 |
the dynamic model is the first reranker integrating search and learning for dependency parsing---reranking , the proposed model integrates search and learning by utilizing a dynamic action | 1 |
semantic role labeling ( srl ) is one of the basic natural language processing ( nlp ) problems---semantic role labeling ( srl ) is a form of shallow semantic parsing | 1 |
the anaphor is a pronoun and the referent is in the cache ( in focus )---if the anaphor is a pronoun , the cache is searched for a plausible referent | 1 |
we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model---we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting | 1 |
the ko-ou relation is a kind of concord , also referring to a sort of bound relation that a ko element appearing in a sentence is followed by an ou element in the latter part of the same sentence---the ko-ou relation is the grammatical form which can be helpful for understanding the sentence meaning at the early stage | 1 |
we parse the senseval test data using the stanford parser generating the output in dependency relation format---we extract the corresponding feature from the output of the stanford parser | 1 |
although machine translation ( mt ) is a very active research field which is receiving an increasing amount of attention from the research community , the results that current mt systems are capable of producing are still quite far away from perfection---machine translation ( mt ) is a highly complex application domain... | 1 |
we measure the overall translation quality using 4-gram bleu , which is computed on tokenized and lowercased data for all systems---we evaluate the performance of different translation models using both bleu and ter metrics | 1 |
we report the mt performance using the original bleu metric---we use bleu as the metric to evaluate the systems | 1 |
in particular , we adopt the approach of phrase-based statistical machine translation---in this work , we apply a standard phrase-based translation system | 1 |
the targetside 4-gram language model was estimated using the srilm toolkit and modified kneser-ney discounting with interpolation---n-gram language models for different orders with interpolated kneser-ney smoothing as well as entropy based pruning were built for this morph lexicon using the srilm toolkit | 1 |
we use the pre-trained glove vectors to initialize word embeddings---for the classification task , we use pre-trained glove embedding vectors as lexical features | 1 |
the decoder is capable of both cnf parsing and earley-style parsing with cube-pruning---the decoder uses a cky-style parsing algorithm to integrate the language model scores | 1 |
extraction of pos tags was performed using the postaggerannotator from the stanford corenlp suite---the tagging was performed using the stanford corenlp software 21 and the robust accurate statistical parsing 22 system , respectively | 1 |
we measured performance using the bleu score , which estimates the accuracy of translation output with respect to a reference translation---under the maximum entropy framework , evidence from different features can be combined with no assumptions of feature independence | 0 |
results are reported using case-insensitive bleu with a single reference---the translation quality is evaluated by case-insensitive bleu-4 metric | 1 |
the weights of the different feature functions were optimised by means of minimum error rate training---feature weights are tuned using minimum error rate training on the 455 provided references | 1 |
we proposed an algorithm that has shown to be able to assess the quality of forum posts---in lin et al , kl and js divergences between human and machine summary distributions were used to evaluate content selection | 0 |
takamura et al propose using spin models for extracting semantic orientation of words---takamura et al proposed using spin models for extracting semantic orientation of words | 1 |
we use a set of 318 english function words from the scikit-learn package---we used the implementation of the scikit-learn 2 module | 1 |
alternately , we could assume that there is no change in the distribution of labels given text , i.e. , math-w-3-7-1-87---that math-w-11-3-0-118 is constant , since the latter does not account for differences in the distribution of text | 1 |
this paper suggests that the language understanding process can be effectively modeled as the statistical outcome of a large number of independent activities occurring in parallel---the target language model is trained by the sri language modeling toolkit on the news monolingual corpus | 0 |
moreover , xu and sun proposed a dependency-based gated recursive model which merges the benefits of the two models above---xu and sun proposed a dependency-based gated recursive neural network to efficiently integrate local and long-distance features | 1 |
combinatory categorial grammar is a lexicalized grammar formalism that has been used for both broad coverage syntactic parsing and semantic parsing---combinatory categorial grammar is a syntactic theory that models a wide range of linguistic phenomena | 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---from yahoo ! answers , we experimentally demonstrate that higher-order methods are broadly applicable to alignment and language models , across both word and synta... | 0 |
trigram language models were estimated using the sri language modeling toolkit with modified kneser-ney smoothing---we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
we apply sri language modeling toolkit to train a 4-gram language model with kneser-ney smoothing---for building the baseline smt system , we used the open-source smt toolkit moses , in its standard setup | 0 |
other approaches are based on external features allowing to cope with various mt systems , eg---other approaches are based on external features allowing to deal with various mt systems , eg | 1 |
in this work , we present a hybrid learning method for training task-oriented dialogue systems through online user interactions---in this work , we focus on training task-oriented dialogue systems through user interactions | 1 |
barzilay and mckeown , 2001 , applied text alignment to parallel translations of a single text and used a part-of-speech tagger to obtain paraphrases---barzilay and mckeown used a corpus-based method to identify paraphrases from a corpus of multiple english translations of the same source text | 1 |
in mikolov et al , the authors are able to successfully learn word translations using linear transformations between the source and target word vector-spaces---mikolov et al proposed a method to use distributed representation of words and learns a linear mapping between vector space of different languages | 1 |
we use 300 dimension word2vec word embeddings for the experiments---we train the cbow model with default hyperparameters in word2vec | 1 |
we use word2vec as the vector representation of the words in tweets---we used word2vec to preinitialize the word embeddings | 1 |
experiments show that our models perform better than the distance-based model and the regular msd model---algorithms show that our methods produce more accurate reordering models , as can be shown by an increase over the regular msd models | 1 |
in this paper , we present an online large margin based training framework for deterministic parsing using nivre¡¯s shift-reduce parsing algorithm---in the remaining part of the paper , we introduce nivre ¡¯ s parsing algorithm , propose a framework for online learning for deterministic parsing | 1 |
distributed word embeddings are learned using a skip-gram recurrent neural net architecture running over a large raw corpus---the skip-gram model adopts a neural network structure to derive the distributed representation of words from textual corpus | 1 |
here , we extract unigram and bigram features and use them in a logistic regression classifier with elastic net regularization---we use a linear regression algorithm with an elastic net regularizer as implemented in scikitlearn | 1 |
a 5-gram language model was created with the sri language modeling toolkit and trained using the gigaword corpus and english sentences from the parallel data---dependency parsing is the task of building dependency links between words in a sentence , which has recently gained a wide interest in the natural language proc... | 0 |
the attention strategies have been widely used in machine translation and question answering---attention has recently been used with considerable empirical success in tasks such as translation and image caption generation | 1 |
with word embeddings , each word is linked to a vector representation in a way that captures semantic relationships---the language model is a 5-gram lm with modified kneser-ney smoothing | 0 |
best¨cworst scaling ( bws ) is a less-known , and more recently introduced , variant of comparative annotation---worst scaling ( bws ) is an alternative method of annotation that is claimed to produce high-quality annotations | 1 |
the skipgram model is used to train word vectors---we use the skipgram model to learn word embeddings | 1 |
zhu et al also use wikipedia to learn a sentence simplification model which is able to perform four rewrite operations , namely substitution , reordering , splitting , and deletion---semantic parsing is the task of mapping natural language sentences to a formal representation of meaning | 0 |
in contrast , our model puts forward the idea of user-product attention by utilizing the global user preference and product characteristics---with the user and product attention , our model can take account of the global user preference and product characteristics | 1 |
we build a 9-gram lm using srilm toolkit with modified kneser-ney smoothing---we use srilm for n-gram language model training and hmm decoding | 1 |
we apply the rules to each sentence with its dependency tree structure acquired from the stanford parser---for collapsed syntactic dependencies we use the stanford dependency parser | 1 |
we use the publicly available 300-dimensional word vectors of mikolov et al , trained on part of the google news dataset---in this paper , we demonstrate that significant gains can instead be achieved by using a more constrained , linguistically motivated grammar | 0 |
to rerank the candidate texts , we used a 5-gram language model trained on the europarl corpus using kenlm---the models are built using the sri language modeling toolkit | 0 |
zhu et al also use wikipedia to learn a sentence simplification model which is able to perform four rewrite operations , namely substitution , reordering , splitting , and deletion---a 5-gram language model was created with the sri language modeling toolkit and trained using the gigaword corpus and english sentences fr... | 0 |
as is now standard for feature-based grammars , we mainly use log-linear models for parse selection---as is now standard for feature-based grammars , we use log-linear models for parse selection | 1 |
we show that while wen et al.¡¯s dataset is more than twice larger than ours , it is less diverse both in terms of input and in terms of text---while wen et al . ¡¯ s dataset is more than twice larger than ours , it is less diverse both in terms of input and in terms of text | 1 |
in one study , collobert et al have formulated the nlp tasks of parts of speech tagging , chunking , named entity recognition and semantic role labeling as multi-task learning problem---collobert et al propose a multi-task learning framework with dnn for various nlp tasks , including part-of-speech tagging , chunking ,... | 1 |
these embedding vectors have been shown to improve a variety of language tasks including named entity recognition , phrase chunking , relation extraction , and part of speech induction---it has been shown that the continuous space representations improve performance in a variety of nlp tasks , such as pos tagging , sem... | 1 |
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