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framenet is an expert-built lexical-semantic resource incorporating the theory of frame-semantics---semantic similarity is a well established research area of natural language processing , concerned with measuring the extent to which two linguistic items are similar ( cite-p-13-1-1 ) | 0 |
the approach was further extended by ionescu , popescu , and cahill to combine several string kernels via multiple kernel learning---all text was tokenized and lemmatized using the treetagger for english | 0 |
a 3-gram language model was trained from the target side of the training data for chinese and arabic , using the srilm toolkit---mihalcea et al compared knowledgebased and corpus-based methods , using word similarity and word specificity to define one general measure of text semantic similarity | 0 |
for our experiments we used the moses phrasebased smt toolkit with default settings and features , including the five features from the translation table , and kb-mira tuning---the vocabularies are generated with byte-pair encoding | 0 |
we adopt a long short-term memory network for the word-level and sentence-level feature extraction---we use the automatic mt evaluation metrics bleu , meteor , and ter , to evaluate the absolute translation quality obtained | 0 |
conditional random field was an extension of both maximum entropy model and hidden markov models that was firstly introduced by---conditional random field is an extension of both maximum entropy model and hidden markov models , which was firstly introduced by lafferty | 1 |
therefore , we expand the property context with additional words based the technique of word embedding---lastly , we populate the adjacency with a distributional similarity measure based on word2vec | 1 |
eriguchi et al use a tree-based lstm to encode input sentence into context vectors---tai et al model the texts through tree-structured lstm , which can be viewed as the combination of recnn and rnn | 1 |
we show that for such sentences , a multi-sentence translation is preferred by readers in terms of flow and understandability---in contrast to previous statistical learning approaches , we directly translate math word problems | 0 |
relation extraction is the task of finding semantic relations between two entities from text---context-free grammar augmented with λ-operators is learned given a set of training sentences and their correct logical forms | 0 |
furthermore , we employ unsupervised topic models to detect the topics of the queries as well as to enrich the target taxonomy---instead , we apply lda topic modeling which requires only an adequate amount of raw text in the target language | 1 |
zeng et al propose the use of position feature for improving the performance of cnn in relation classification---zeng et al use convolutional neural network for learning sentence-level features of contexts and obtain good performance even without using syntactic features | 1 |
madamira is a tool , originally designed for morphological analysis and disambiguation of msa and dialectal arabic texts---in this paper , we name the problem of choosing the correct word from the homophone set | 0 |
charitakis used uplug for aligning words in a greek-english parallel corpus---in charitakis uplug was used for aligning words in a greek-english parallel corpus | 1 |
accurate learning of inference knowledge , such as entailment rules , has become critical for further progress of applied semantic systems---becker , rambow , and niv argue that even linear context-free rewriting systems are not powerful enough to describe scrambling | 0 |
we use a shared subword vocabulary by applying byte-pair encoding to the data for all variants concatenated---we use byte pair encoding with 45k merge operations to split words into subwords | 1 |
for word-level embedding e w , we utilize pre-trained , 300-dimensional embedding vectors from glove 6b---we use 300-dimensional glove vectors trained on 6b common crawl corpus as word embeddings , setting the embeddings of outof-vocabulary words to zero | 1 |
also , dependency relations successfully differentiate the generic concepts from the domain-specific concepts , so that the slu model is able to predict more coherent set of semantic slots---it is shown that the structure of semantic concepts helps decide domain-specific slots and further improves the slu performance | 1 |
moreover , arabic is a morphologically complex language---arabic is a morphologically rich language , in which a word carries not only inflections but also clitics , such as pronouns , conjunctions , and prepositions | 1 |
our results demonstrate that such use of eye gaze can potentially compensate for a conversational systems limited language processing and domain modeling capability ( cite-p-16-5-2 )---demonstrating a close relationship between language production and eye gaze , our previous work has incorporated naturally occurring eye gaze in reference resolution ( cite-p-16-5-2 ) | 1 |
the model parameters of word embedding are initialized using word2vec---we use the pre-trained word2vec embeddings provided by mikolov et al as model input | 1 |
sun and xu enhanced a cws model by interpolating statistical features of unlabeled data into the crfs model---phrase-based mt models consider translation as a mapping of small text chunks , with possible reordering | 0 |
compared with the state of the art scope detection systems , our system achieves substantial improvement---compared with the state of the art scope detection systems , our system achieves the performance of accuracy | 1 |
we presented a series of experiments for automatic prediction of the latent features of functional gender and number , and rationality in arabic---in this paper , we present results on the task of automatic identification of functional gender , number and rationality of arabic | 1 |
a selection of images from the nimstim set of facial expressions was used for the rating task---the data used in the emotion rating task was taken from the nimstim set of facial expressions | 1 |
following the framework of cite-p-12-1-12 , we use amazon ’ s mechanical turk service to produce the first publicly available 1 dataset of negative deceptive opinion spam , containing 400 gold standard deceptive negative reviews of 20 popular chicago hotels---we applied liblinear via its scikitlearn python interface to train the logistic regression model with l2 regularization | 0 |
for the experiment purpose , we extract body content in every web page by using noise reducing algorithm---for the experimental purpose , we extract body content in every web page by using noise reducing algorithm | 1 |
we build all the classifiers using the l2-regularized linear logistic regression from the liblinear package---for this model , we use a binary logistic regression classifier implemented in the lib-linear package , coupled with the ovo scheme | 1 |
for wikipedia , our best method obtains a median prediction error of just 11.8 kilometers---the evaluation metric is casesensitive bleu-4 | 0 |
over the last decade , phrase-based statistical machine translation systems have demonstrated that they can produce reasonable quality when ample training data is available , especially for language pairs with similar word order---phrase-based statistical machine translation models have achieved significant improvements in translation accuracy over the original ibm word-based model | 1 |
we evaluated our mt output using the surface based evaluation metrics bleu , meteor , cder , wer , and ter---we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus | 0 |
this paper proposes an input-splitting method for robust spoken-language translation---we have proposed an input-splitting method for translating spoken-language | 1 |
bengio et al proposed a probabilistic neural network language model for word representations---bengio et al have proposed a neural network based model for vector representation of words | 1 |
it can utilize existing information about word ordering present in the target hypotheses---and also to utilize existing information about word ordering present in the target hypotheses | 1 |
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---we first obtain word representations using the popular skip-gram model with negative sampling introduced by mikolov et al and implemented in the gensim package | 1 |
methods for fine-grained sentiment analysis are developed by hu and liu , ding et al and popescu and etzioni---fine-grained sentiment analysis methods have been developed by hatzivassiloglou and mckeown , hu and liu and popescu and etzioni , among others | 1 |
coreference resolution is the task of automatically grouping references to the same real-world entity in a document into a set---coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world | 1 |
the language model was constructed using the srilm toolkit with interpolated kneser-ney discounting---the srilm toolkit was used for training the language models using kneser-ney smoothing | 1 |
universal dependencies ( ud ) ( cite-p-13-3-16 ) is a collection of treebanks for a variety of languages , annotated with a scheme optimised for knowledge transfer---universal dependencies ( ud ) ( cite-p-20-3-4 ) is a cross-linguistically consistent annotation scheme for dependency-based treebanks | 1 |
conditional random fields are discriminatively-trained undirected graphical models that find the globally optimal labeling for a given configuration of random variables---conditional random fields are undirected graphical models trained to maximize the conditional probability of the desired outputs given the corresponding inputs | 1 |
in future work , we would like to extend the clustering algorithm to not use a fixed number of target clusters but to depend on the number of natural clusters the data falls into---lin et al develop a sentence-level recurrent neural network language model that takes a sentence as input and tries to predict the next one based on the sentence history vector | 0 |
moreover , we conduct a task-based evaluation by incorporating these triples as additional features into document classification and enhances the performance by 3.02 %---in the task-based evaluation , the enriched model derived from the triples of background knowledge performs better by 3 . 02 % , which demonstrates the effectiveness of our framework | 1 |
we used adam for optimization of the neural models---our models are implemented with pytorch , optimized with adam | 1 |
researchers then began to experiment with weakly supervised machine learning algorithms such as cotraining---so , researchers in nlp began to experiment with weakly supervised machine learning algorithms such as co-training | 1 |
existing studies on semantic parsing mainly focus on the in-domain setting---training of semantic parsing can be quite effective under a domain adaptation setting | 1 |
the presented approach requires a restriction on the entity-tuple embedding space---restriction of the entity-tuple embedding space does not hurt the expressiveness of the model | 1 |
word sense disambiguation ( wsd ) is a task to identify the intended sense of a word based on its context---word sense disambiguation ( wsd ) is the task of determining the correct meaning or sense of a word in context | 1 |
following , we develop a continuous bag-of-words model that can effectively model the surrounding contextual information---work presents a single , joint model for parsing and word alignment | 0 |
we first provided a general framework of crf training based on mce criterion---as the above studies , we first propose a generalization framework for crf training | 1 |
in 1995 kessler introduced the use of the levenshtein distance as a tool for measuring linguistic distances between dialects---kessler introduced the use of string edit distance measure as a means of calculating the distance between the pronunciations of corresponding words in different dialects | 1 |
the second one is machine learning approach which uses annotated texts with a given labels to learn a classification model , an early work was done on a movie review dataset---the second one is machine learning approach which uses annotated texts with a given label to learn a statistical model and an early work was done on a movie review dataset , pang , lee et al , 2002 combined to achieve a better performance | 1 |
both of these are represented as a probabilistic distribution of words across verbs---of these triples , and are defined across verbs based on probabilistic topic distributions | 1 |
the problem addressed in this article is thus to segment a multilingual text by language and identify the language of each segment---the problem addressed in this paper is to segment a given multilingual document into segments for each language | 1 |
that way we try to overcome the plateauing in performance in coreference resolution observed by cite-p-17-1-10---in the manner we described will assist the research on coreference resolution to overcome the plateauing in performance observed by cite-p-17-1-10 | 1 |
each occurrence of a candidate word in text is represented as a vector of features---word in text is represented as a vector of features derived from a small context window | 1 |
for evaluation , we compare each summary to the four manual summaries using rouge---we use the rouge toolkit for evaluation of the generated summaries in comparison to the gold summaries | 1 |
in addition , we compare against the morfessor categories-map system---we use standard phrase-based smt techniques to build separate phrase tables for the indonesian-english and the malay-english bitexts | 0 |
rhetorical structure theory posits a hierarchical structure of discourse relations between spans of text---much recent work on language generation has made use of discourse representations based on rhetorical structure theory | 1 |
klementiev et al treated the task as a multi-task learning problem where each task corresponds to a single word , and the task relatedness is derived from cooccurrence statistics in bilingual parallel corpora---klementiev et al treat the task as a multi-task learning problem where each task corresponds to a single word , and task relatedness is derived from co-occurrence statistics in bilingual parallel data | 1 |
this is opposite to the conclusion in indomain tasks that using only adjectives as features results in much worse performance than using the same number of most frequent unigrams---indeed , it was resulted in that using only adjectives as features actually results in much worse performance than using the same number of most frequent unigrams | 1 |
for both languages , we used the srilm toolkit to train a 5-gram language model using all monolingual data provided---we apply sri language modeling toolkit to train a 4-gram language model with kneser-ney smoothing | 1 |
recently , mikolov et al proposed novel model architectures to compute continuous vector representations of words obtained from very large data sets---recently , mikolov et al introduced an efficient way for inferring word embeddings that are effective in capturing syntactic and semantic relationships in natural language | 1 |
the word vectors of vocabulary words are trained from a large corpus using the glove toolkit---these word vectors can be randomly initialized , or be pre-trained from text corpus with learning algorithms | 1 |
in this paper , our coreference resolution system for conll-2012 shared task is summarized---word sense disambiguation ( wsd ) is formally defined as the task of computationally identifying senses of a word in a context | 0 |
with our test collection , we construct a baseline using lucene¡¯s default implementation of the vector space model ( vsm )---coreference resolution is the task of determining which mentions in a text refer to the same entity | 0 |
in this paper , we address above challenges in active learning for imbalanced sentiment classification---in this paper , we focus on the imbalanced class distribution scenario for sentiment classification | 1 |
the well-known phrasebased translation model has significantly advanced the progress of smt by extending translation units from single words to phrases---phrase-based statistical machine translation models have achieved significant improvements in translation accuracy over the original ibm word-based model | 1 |
relation extraction ( re ) has been defined as the task of identifying a given set of semantic binary relations in text---we also report the results using bleu and ter metrics | 0 |
we applied a topic modelling approach to this task , and contrasted it with baseline and benchmark methods---we demonstrate that an lda-based topic modelling approach outperforms a baseline distributional semantic approach and weighted textual matrix | 1 |
in addition , we explore methods to improve phrase structure parsing for learner english---in our future work , we will evaluate parsing performance on other learner corpora | 1 |
wordnet is a key lexical resource for natural language applications---wordnet is a byproduct of such an analysis | 1 |
we used srilm to build a 4-gram language model with interpolated kneser-ney discounting---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 1 |
we initialize the embedding weights by the pre-trained word embeddings with 200 dimensional vectors---we convert the question into a sequence of learned word embeddings by looking up the pre-trained vectors , such as glove | 1 |
examples of well-known srl schemes motivated by different linguistic theories are framenet , propbank , and verbnet---there are several well-established , large-scale repositories of semantic frames for general language , eg , verbnet , propbank and framenet | 1 |
collobert et al first introduced an end-to-end neural-based approach with sequence-level training and uses a convolutional neural network to model the context window---collobert et al designed a unified neural network to learn distributed representations that were useful for part-of-speech tagging , chunking , ner , and semantic role labeling | 1 |
the head transducer model was trained and evaluated on english-to-mandarin chinese translation of transcribed utterances from the atis corpus---both the transfer and transducer systems were trained and evaluated on english-to-mandarin chinese translation of transcribed utterances from the atis corpus | 1 |
erbach , barg and walther and fouvry followed a unification-based symbolic approach to unknown word processing for constraint-based grammars---cussens and pulman used a symbolic approach employing inductive logic programming , while erbach , barg and walther and fouvry followed a unification-based approach | 1 |
among hundreds of product aspects , it is also inefficient for user to browse consumer reviews and opinions on a specific aspect---with such organization , user can easily grasp the overview of consumer reviews , as well as seek consumer reviews and opinions on any specific aspect | 1 |
we show that the class of string languages generated by linear context-free rewriting systems is equal to the class of output languages of deterministic tree-walking transducers---in this section we describe context-free hypergraph gramars since they are an example of a lcfrs involving the manipulation of graphs , zthe class of string languages generated by context-free hypergraph grammars is equal to out ( dtwt ) | 1 |
our model is a structured conditional random field---we trained linear-chain conditional random fields as the baseline | 1 |
we use the word2vec framework in the gensim implementation to generate the embedding spaces---for the document embedding , we use a doc2vec implementation that downsamples higher-frequency words for the composition | 1 |
twitter is a widely used microblogging platform , where users post and interact with messages , “ tweets ”---we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings , which we do not optimize during training | 0 |
we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing---the first confusion network decoding method was based on multiple string alignment borrowed from biological sequence analysis | 0 |
we use a multiplicative-style attention attention architecture---we use an attention-augmented architecture with a bi-directional lstm as encoder | 1 |
relation extraction is the task of recognizing and extracting relations between entities or concepts in texts---relation extraction is a fundamental task in information extraction | 1 |
in this paper , our approach describes how to exploit non-local information to a slu problem---in this paper , we exploit non-local features as an estimate of long-distance dependencies | 1 |
pang et al and turney et al are generally regarded as the start of the research area of sentiment analysis---pang et al were one of the first to experiment with sentiment classification | 1 |
we implemented our method in a phrase-based smt system---a topic is a particular subject that we write about or discuss , and subtopics are represented in pieces of text that cover different aspects of the main topic | 0 |
for all classifiers , we used the scikit-learn implementation---we used the svm implementation of scikit learn | 1 |
the language model was trained using srilm toolkit---the srilm toolkit was used to build the trigram mkn smoothed language model | 1 |
this paper addresses the problem of identifying sen-program---this paper explores the problem of identifying sentence boundaries | 1 |
our method maintains the parameters as a low-rank tensor to obtain low dimensional representations of words in their syntactic roles , and to leverage modularity in the tensor for easy training with online algorithms---to obtain low dimensional representations of words in their syntactic roles , and to leverage modularity in the tensor for easy training with online algorithms | 1 |
automatic text summarization is a rapidly developing field in computational linguistics---automatic text summarization is a seminal problem in information retrieval and natural language processing ( luhn , 1958 ; baxendale , 1958 ; edmundson , 1969 ) | 1 |
semantic role labeling ( srl ) is a major nlp task , providing a shallow sentence-level semantic analysis---semantic role labeling ( srl ) is the task of labeling predicate-argument structure in sentences with shallow semantic information | 1 |
we used pre-trained word vectors of glove , trained on 2 billion words from twitter for english---learned word representations are widely used in nlp tasks such as tagging , named entity recognition , and parsing | 0 |
named entity recognition ( ner ) is the task of identifying and typing phrases that contain the names of persons , organizations , locations , and so on---we measure the translation quality with automatic metrics including bleu and ter | 0 |
in prior work , it has been shown that morphological segmentation of the arabic source benefits the performance of arabic-to-english smt---specifically for arabic-to-english smt , the importance of tokenization using morphological analysis has been shown by many researchers | 1 |
at a time in which constraint-based reasoning is ubiquitous in many branches of science , including in the field of computational linguistics , we must hasten to add that the notion of constraint examined in shieber 's work is quite different from the notion of constraint satisfaction as originally described by cite-p-3-5-12---in many branches of science , including in the field of computational linguistics , we must hasten to add that the notion of constraint examined in shieber ' s work is quite different from the notion of constraint satisfaction | 1 |
coreference resolution is the task of partitioning a set of mentions ( i.e . person , organization and location ) into entities---we trained a trigram language model on the chinese side , with the srilm toolkit , using the modified kneser-ney smoothing option | 0 |
we use the opensource moses toolkit to build a phrase-based smt system---for building the baseline smt system , we used the open-source smt toolkit moses , in its standard setup | 1 |
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