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we split each document into sentences using the sentence tokenizer of the nltk toolkit---we use the tokenizer from nltk to preprocess each sentence | 1 |
we used srilm to build a 4-gram language model with interpolated kneser-ney discounting---we trained two 5-gram language models on the entire target side of the parallel data , with srilm | 1 |
for the evaluation of machine translation quality , some standard automatic evaluation metrics have been used , like bleu , nist and ribes in all experiments---for the evaluation of machine translation quality , some standard automatic evaluation metrics have been used , like bleu and ribes in all experiments | 1 |
distributional semantic models represent the meanings of words by relying on their statistical distribution in text---distributional semantic models are usually the first choice for representing textual items such as words or sentences | 1 |
kalchbrenner et al propose a dynamic cnn model using a dynamic k-max pooling mechanism which is able to generate a feature graph which captures a variety of word relations---we use a minibatch stochastic gradient descent algorithm together with the adam method to train each model | 0 |
traditional semantic space models represent meaning on the basis of word co-occurrence statistics in large text corpora---distributional semantic models are employed to produce semantic representations of words from co-occurrence patterns in texts or documents | 1 |
to the best of our knowledge , there is still no reported study of this problem---among many others , morante and daelemans and li et al propose scope detectors using the bioscope corpus | 0 |
in this paper , we propose to translate from video pixels to natural language with a single deep neural network---we have proposed a model for video description which uses neural networks for the entire pipeline from pixels to sentences | 1 |
we release a dataset of 1,938 annotated posts from across the four forums---in this section , we discuss approaches that are most relevant to our problem | 0 |
in this paper , we focus on modeling inter-text relations induced by twitter/news features---in this paper , we study the problem of mining and exploiting correlations between texts | 1 |
resnik uses selectional preferences of predicates for word sense disambiguation---additionally , resnik demonstrates the influence of implicit direct objects on aspectual classification | 1 |
gamon et al train a decision tree model and a language model to correct errors in article and preposition usage---gamon et al use a decision tree model and a 5-gram language model trained on the english gigaword corpus to correct errors in english article and preposition usage | 1 |
there are various methods such word2vec and global vectors for word representation which create a distributed representation of words---word2vec and glove models are a popular choice for word embeddings , representing words by vectors for downstream natural language processing | 1 |
it has been used to perform named entity disambiguation as well---it has also been applied to the task of named entity disambiguation | 1 |
we use two standard evaluation metrics bleu and ter , for comparing translation quality of various systems---for instance , bahdanau et al advocate the attention mechanism to dynamically generate a context vector of the whole source sentence for improving the performance of the nmt | 0 |
we rely on conditional random fields 1 for predicting one label per reference---we enrich the content of microblogs by inferring the association between microblogs and external words | 0 |
we propose a novel topic model by utilizing the structures of conversations in microblogs---in this work , we organize microblog messages as conversation trees | 1 |
our method is compared with phrasal smt method and the encoder-decoder method , and achieves significant improvement in both bleu and human evaluation---and the results show that our proposed method can achieve outstanding performance , compared with both the traditional smt methods and the existing encoder-decoder models | 1 |
semantic role labeling ( srl ) is the task of identifying the predicate-argument structure of a sentence---semantic role labeling ( srl ) is the process of producing such a markup | 1 |
to evaluate segment translation quality , we use corpus level bleu---we use case-sensitive bleu-4 to measure the quality of translation result | 1 |
third , we apply multitask learning to estimate the neural network parameters jointly---we apply multitask learning ( mtl ) ( caruana , 1997 ) to jointly train related neural network features by sharing parameters | 1 |
unfortunately , we have seen that this kind of theory can not explain opaque indexicals---we use the morphological analyzer mada to decompose the arabic source | 0 |
we used the sri language modeling toolkit to train lms on our training data for each ilr level---we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model | 1 |
the word embeddings are word2vec of dimension 300 pre-trained on google news---the embeddings have been trained with word2vec on twitter data | 1 |
for annotation , we used the brat rapid annotation tool---all annotations were done using the brat rapid annotation tool | 1 |
finally , we combine all the above features using a support vector regression model which is implemented in scikit-learn---in order to obtain a single similarity score , we use the scikit-learn 6 implementation of support vector regression | 1 |
we work with the phrase-based smt framework as the baseline system---our baseline is a standard phrase-based smt system | 1 |
contrary to the vast interest in open ie , its task formulation has been largely overlooked---the use of unsupervised word embeddings in various natural language processing tasks has received much attention | 0 |
this baseline is based on dkpro tc and relies on support vector classification using weka---in this paper , we propose using a constrained word lattice , which encodes input phrases and tm constraints | 0 |
most feature-based models adopt various linguistic features and design complicated rules to recognize implicit discourse relations---by far the most common features used for representing implicit discourse relations are lexical | 1 |
central to our approach is a new type-based sampling algorithm for hierarchical pitman-yor models in which we track fractional table counts---central to the approach is a novel formulation of open ie as a sequence tagging problem , addressing challenges such as encoding multiple extractions for a predicate | 1 |
twitter is a widely used microblogging platform , where users post and interact with messages , “ tweets ”---twitter is a popular microblogging service , which , among other things , is used for knowledge sharing among friends and peers | 1 |
for the actioneffect embedding model , we use pre-trained glove word embeddings as input to the lstm---our word embedding features are based on the recent success of word2vec 4 , a method for representing indidivual words as distributed vectors | 0 |
all english data are pos tagged and lemmatised using the treetagger---all text was tokenized and lemmatized using the treetagger for english | 1 |
furthermore , we retrofit a language model with a label-conditional architecture , which allows the model to augment sentences without breaking the label-compatibility---that is incompatible with the annotated labels of the original sentences , we retrofit the lm with a label-conditional architecture | 1 |
word sense disambiguation ( wsd ) is a key enabling-technology that automatically chooses the intended sense of a word in context---word sense disambiguation ( wsd ) is a task to identify the intended sense of a word based on its context | 1 |
furthermore , xu et al correct false negative instances by using pseudo-relevance feedback to expand the origin knowledge base---wei et al show that instances may be labeled incorrectly due to the knowledge base being incomplete | 1 |
we instead use adagrad , a variant of stochastic gradient descent in which the learning rate is adapted to the data---to learn grsemi-crfs , we employ adagrad , an adaptive stochastic gradient descent method which has been proved successful in similar tasks | 1 |
irony detection is a key task for many natural language processing works---opinion can be obtained by applying natural language processing techniques | 1 |
ding et al used conditional random fields to extract context of questions for answer detection---ding et al used crfs to detect contexts and answers of questions from forum threads | 1 |
agirre and de lacalle worked on the semisupervised da for wsd---agirre and de lacalle worked on the semi-supervised da of wsd | 1 |
language models are built using the sri-lm toolkit---trigram language models are implemented using the srilm toolkit | 1 |
in particular , we use a rnn based on the long short term memory unit , designed to avoid vanishing gradients and to remember some long-distance dependences from the input sequence---in order to map queries and documents into the embedding space , we make use of recurrent neural network with the long short-term memory architecture that can deal with vanishing and exploring gradient problems | 1 |
ikeda et al proposed a hybrid-based method using both text and community membership---ikeda et al and sakaki et al used methods that incorporate information | 1 |
we have used the srilm with kneser-ney smoothing for training a language model for the first stage of decoding---we use srilm to build 5-gram language models with modified kneser-ney smoothing | 1 |
the ape system for each target language was tuned on comparable development sets , optimizing ter with minimum error rate training---the weights of the different feature functions were tuned by means of minimum error-rate training executed on the europarl development corpus | 1 |
marcu and echihabi demonstrated that word pairs extracted from the respective text spans are a good signal of the discourse relation between arguments---marcu and echihabi presented the unsupervised approach to recognize the discourse relations by using word pair probabilities between two adjacent sentences | 1 |
for this task , we use glove pre-trained word embedding trained on common crawl corpus---to train our neural algorithm , we apply word embeddings of a look-up from 100-d glove pre-trained on wikipedia and gigaword | 1 |
support vector machine is highly effective on traditional document categorization---as noted in joachims , support vector machines are well suited for text categorisation | 1 |
word sense disambiguation ( wsd ) is the task of identifying the correct sense of an ambiguous word in a given context---we measure the quality of the automatically created summaries using the rouge measure | 0 |
in this paper , we use this idea to combine classifiers that were trained for two different tasks on different datasets using constraints to encode linguistic knowledge---following budanitsky and hirst , we estimate the wordnet sense similarity using the method proposed by jiang and conrath | 0 |
segmentation is the task of dividing a stream of data ( text or other media ) into coherent units---segmentation is the task of splitting up an item , such as a document , into a sequence of segments by placing boundaries within | 1 |
we use the linear kernel 6 svm , as our text classifier---the context sensitive constraints are expressed in a version of restriction language which is compiled into lisp | 0 |
moreover , back translation approaches show efficient use of monolingual data to improve neural machine translation---a context-free grammar ( cfg ) is a 4-tuple math-w-4-1-0-9 , where math-w-4-1-0-18 is the set of nonterminals , σ the set of terminals , math-w-4-1-0-31 the set of production rules and math-w-4-1-0-38 a set of starting nonterminals ( i.e . multiple starting nonterminals are possible ) | 0 |
for the srl module , we use a rich syntactic feature-based learning method---in the srl module , we use the training data provided by semeval-2010 | 1 |
this means in practice that the language model was trained using the srilm toolkit---a 5-gram language model was built using srilm on the target side of the corresponding training corpus | 1 |
firstly , we built a forward 5-gram language model using the srilm toolkit with modified kneser-ney smoothing---we train a 4-gram language model on the xinhua portion of the english gigaword corpus using the srilm toolkits with modified kneser-ney smoothing | 1 |
the experimental results demonstrate that our models outperform the baselines on five word similarity datasets---such measures as mutual information , latent semantic analysis , log-likelihood ratio have been proposed to evaluate word semantic similarity based on the co-occurrence information on a large corpus | 0 |
in the pos tag level , we basically used the universal tag-set proposed by petrov et al in mapping original tags into universal ones---we use the universal pos tagset proposed by petrov et al which has 12 pos tags that are applicable to both en and hi | 1 |
however , none of these models includes any contextual information beyond the neighbouring words---however , these models typically integrate only limited additional contextual information | 1 |
durrani et al proposed a joint sequence model for the translation and reordering probabilities---durrani et al developed a joint model that captures translation of contiguous and gapped units as well as reordering | 1 |
in order to do so , we use the moses statistical machine translation toolkit---finally , we extract the semantic phrase table from the augmented aligned corpora using the moses toolkit | 1 |
for convenience we will will use the rule notation of simple rcg , which is a syntactic variant of lcfrs , with an arguably more transparent notation---we use a count-based distributional semantics model and the continuous bag-of-words model to learn word vectors | 0 |
we used conditional random fields for the machine learning task---1 the atb comprises manually annotated morphological and syntactic analyses of newswire text from different arabic sources | 0 |
we evaluate the translation quality using the case-insensitive bleu-4 metric---we will show translation quality measured with the bleu score as a function of the phrase table size | 1 |
case-insensitive bleu-4 is our evaluation metric---we used minimum error rate training to optimize the feature weights | 0 |
this means in practice that the language model was trained using the srilm toolkit---the language model is trained on the target side of the parallel training corpus using srilm | 1 |
hosseini et al solve single step or multistep homogeneous addition and subtraction problems by learning verb categories from the training data---hosseini et al solve addition and subtraction problems by learning to categorize verbs for the purpose of updating a world representation derived from the problem text | 1 |
the first statistical machine translation system we used is the off-the-shelf moses toolkit---our baseline is an in-house phrase-based statistical machine translation system very similar to moses | 1 |
we use the glove vectors of 300 dimension to represent the input words---we initialize the embedding weights by the pre-trained word embeddings with 200 dimensional vectors | 1 |
the word embeddings are pre-trained , using word2vec 3---coreference resolution is a task aimed at identifying phrases ( mentions ) referring to the same entity | 0 |
hochreiter and schmidhuber , 1997 ) proposed a long short-term memory network , which can be used for sequence processing tasks---the long short-term memory was first proposed by hochreiter and schmidhuber that can learn long-term dependencies | 1 |
marcu and wong proposed a phrase-based context-free joint probability model for lexical mapping---marcu and wong presented an ambitious maximum likelihood model and em inference algorithm for learning phrasal translation representations | 1 |
cui et al proposed a system utilizing fuzzy relation matching guided by statistical models---cui et al showed that their fuzzy relation syntactic matching method outperformed the density-based methods | 1 |
word sense disambiguation ( wsd ) is a problem of finding the relevant clues in a surrounding 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 |
mikolov et al further proposed continuous bagof-words and skip-gram models , which use a simple single-layer architecture based on inner product between two word vectors---although wordnet is a fine resources , we believe that ignoring other thesauri is a serious oversight | 0 |
for the semantic language model , we used the srilm package and trained a tri-gram language model with the default goodturing smoothing---in addition , we use an english corpus of roughly 227 million words to build a target-side 5-gram language model with srilm in combination with kenlm | 1 |
zhao and vogel combine a sentence length model with an ibm model 1-type translation model---zhao and vogel describe a generative model for discovering parallel sentences in the xinhua news chineseenglish corpus | 1 |
transliteration is often defined as phonetic translation ( cite-p-21-3-2 )---transliteration is a subtask in ne translation , which translates nes based on the phonetic similarity | 1 |
latent semantic analysis has been used to reduce the dimensionality of semantic spaces leading to improved performance---latent semantic analysis is used to measure semantic similarity between each pair of words | 1 |
we used the phrasebased smt system moses to calculate the smt score and to produce hfe sentences---for our baseline we use the moses software to train a phrase based machine translation model | 1 |
brown clustering is a kind of word representations , which assigns word with similar functions to the same cluster---clustering is a popular technique for unsupervised text analysis , often used in industrial settings to explore the content of large amounts of sentences | 1 |
this implies that convkb generalizes transitional characteristics in transition-based embedding models---so that convkb generalizes the transitional characteristics in the transition-based embedding models | 1 |
similarity is a fundamental concept in theories of knowledge and behavior---similarity is a kind of association implying the presence of characteristics in common | 1 |
to create the word clusters , we employ brown clustering , a hierarchical clustering algorithm proposed by---we used the brown word clustering algorithm to obtain the word clusters | 1 |
we use 100-dimension glove vectors which are pre-trained on a large twitter corpus and fine-tuned during training---the language models are 4-grams with modified kneser-ney smoothing which have been trained with the srilm toolkit | 0 |
in spite of this wide attention , open ie ’ s formal definition is lacking---in spite of this broad attention , the open ie task definition has been lacking | 1 |
this produces multiple paths between terms , allowing the sash to shape itself to the data set---for example , blitzer et al proposed a domain adaptation method based on structural correspondence learning | 0 |
sentiment analysis is the computational analysis of people ’ s feelings or beliefs expressed in texts such as emotions , opinions , attitudes , appraisals , etc . ( cite-p-11-3-3 )---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 ) | 1 |
the language model is trained with the sri lm toolkit , on all the available french data without the ted data---a 4-gram language model is trained on the monolingual data by srilm toolkit | 1 |
we evaluated each sentence compression method using word f -measures , bigram f -measures , and bleu scores---word sense disambiguation ( wsd ) is the task of determining the meaning of a word in a given context | 0 |
the elan annotation tool was used for transcription of parent and child utterances , as well as annotation of eye gaze , deictic gestures and object manipulation---hammarstr枚m and borin give an extensive overview of stateof-the-art unsupervised learning of morphology | 0 |
pang and lee cast this problem a classification task , and use machine learning method in a supervised learning framework---pang et al considered the same problem and presented a set of supervised machine learning approaches to it | 1 |
we distinguish the sublanguages of mrs nets and normal dominance nets , and show that they can be intertranslated---we have distinguished the sublanguages of mrs-nets and normal dominance nets that are sufficient to model scope | 1 |
a pun is a means of expression , the essence of which is in the given context the word or phrase can be understood in two meanings simultaneously ( cite-p-22-3-7 )---the pun is defined as “ a joke exploiting the different possible meanings of a word or the fact that there are words which sound alike but have different meanings ” ( cite-p-7-1-6 ) | 1 |
for feature building , we use word2vec pre-trained word embeddings---we use the pre-trained word2vec embeddings provided by mikolov et al as model input | 1 |
therefore , one model can share translations and even derivations with other models---and elsner et al . ( 2009 ) focused specifically on names and discovering their structure , which is a part of the problem | 0 |
there have been many studies on computing similarities between words based on their distributional similarity---many methods have been proposed to compute distributional similarity between words | 1 |
the log-linear feature weights are tuned with minimum error rate training on bleu---weights are optimized by mert using bleu as the error criterion | 1 |
we used the stanford parser to parse each of the reviews and the natural language toolkit to post process the results---we used nltk to tokenize the reviews , and employed the wikipedia list of common misspellings to correct misspelled words | 1 |
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