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we use the scikit-learn toolkit as our underlying implementation---we employ scikit-learn for building our classifiers | 1 |
phonetic translation across these pairs is called transliteration---transliteration is a process of rewriting a word from a source language to a target language in a different writing system using the word ’ s phonological equivalent | 1 |
the clear drawback of supervised methods is the need of training data : labeled data is expensive to obtain , and there is often a mismatch between the training data and the data the system will be applied to---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 0 |
moreover , the nws scores shows interesting correlations with perceived meaning of words indicated by concreteness and imageability psycholinguistic ratings---scores demonstrate a moderate level of correlation with concreteness and imageability ratings , despite not being specifically trained to predict such psycholing... | 1 |
we evaluated the translation quality using the bleu-4 metric---for the evaluation of translation quality , we used the bleu metric , which measures the n-gram overlap between the translated output and one or more reference translations | 0 |
in this paper , we propose a new method for generating diverse hypotheses from a single mt system using traits---in this paper , we show it is possible to create diverse input hypotheses for combination | 1 |
conditional random fields are global discriminative learning algorithms for problems with structured output spaces , such as dependency parsing---conditional random fields constitute a widely-used and effective approach for supervised structure learning tasks involving the mapping between complex objects such as string... | 1 |
coreference resolution is a task aimed at identifying phrases ( mentions ) referring to the same entity---coreference resolution is a field in which major progress has been made in the last decade | 1 |
in this run , we use a sentence vector derived from word embeddings obtained from word2vec---we competed in subtask 1 and 2 which consist respectively in identifying all the key phrases in scientific publications and label them | 0 |
we compare the results of ensemble decoding with a number of baselines for domain adaptation---three out of the four categories of features can be inferred from an image-question pair | 0 |
we use the long short-term memory architecture for recurrent layers---we use a standard long short-term memory model to learn the document representation | 1 |
we use the logistic regression implementation of liblinear wrapped by the scikit-learn library---we use the logistic regression classifier in the skll package , which is based on scikit-learn , optimizing for f 1 score | 1 |
curran and moens have demonstrated that dramatically increasing the volume of raw input text used to extract context information significantly improves the quality of extracted synonyms---the language model is trained on the target side of the parallel training corpus using srilm | 0 |
marcu and echihabi , 2002 ) used several patterns to extract instances of discourse relations such as contrast and elaboration from unlabeled corpora---matsubayashi , okazaki , and tsujii propose to exploit the relations between semantic roles in an attempt to overcome the scarcity of frame-specific role annotations | 0 |
in this paper , we present the first deep learning architecture designed to capture metaphorical composition---in this paper , we introduced the first deep learning architecture designed to capture metaphorical composition | 1 |
in order to deal with this challenge we rely on the negative sampling approach of mikolov et al---we rely on distributed representation based on the neural network skip-gram model of mikolov et al | 1 |
our 5-gram language model was trained by srilm toolkit---the language model is trained and applied with the srilm toolkit | 1 |
multiword expressions are problematic in machine translation due to the idiomaticity and overgeneration problems---multiword expressions are a major obstacle that hinder precise natural language processing | 1 |
the smt system was tuned on the development set newstest10 with minimum error rate training using the bleu error rate measure as the optimization criterion---system tuning was carried out using both k-best mira and minimum error rate training on the held-out development set | 1 |
in this paper , we present a unified model for word sense representation and disambiguation that uses one representation per sense---we trained a trigram language model on the chinese side , with the srilm toolkit , using the modified kneser-ney smoothing option | 0 |
for the feature-based system we used logistic regression classifier from the scikit-learn library---we implemented the algorithms in python using the stochastic gradient descent method for nmf from the scikit-learn package | 1 |
the word embeddings are word2vec of dimension 300 pre-trained on google news---word embeddings have been trained using word2vec 4 tool | 1 |
in this work , we investigate large-scale , discriminative itg word alignment---we proposed a general method to watermark and probabilistically identify the structured outputs of machine learning algorithms | 0 |
moreover , the pos tagging could be efficiently and effectively resolved over subword sequences---in practice , the decoding for pos tagging over subwords is efficient | 1 |
we use the baseline model of hockenmaier and steedman , which is a simple generative model that is equivalent to an unlexicalized pcfg---one solution is to consider only the normal-form derivation , which is the route taken in hockenmaier and steedman | 1 |
in other words , we will adapt the word alignment information in the general domain to the specific domain---by adapting the word alignment information in the general domain to the specific domain | 1 |
inference rules are important in many fields such as question answering , textual entailment and information extraction---inference rules are an important building block of many semantic applications , such as question answering and information extraction | 1 |
stance detection is the task of assigning stance labels to a piece of text with respect to a topic , i.e . whether a piece of text is in favour of “ abortion ” , neutral , or against---stance detection is the task of estimating whether the attitude expressed in a text towards a given topic is ‘ in favour ’ , ‘ against ... | 1 |
mikolov et al and mikolov et al further observe that the semantic relationship of words can be induced by performing simple algebraic operations with word vectors---as mentioned above , mikolov et al suggested to capture the relations between words as the offset of their vector embeddings | 1 |
we used the stanford parser to generate dependency trees of sentences---the conversion to dependency trees was done using the stanford parser | 1 |
our neural ecd models outperform the prior state-of-the-art by significant margins---our neural model for ecd exceptionally boosts the state-of-the-art detection | 1 |
soricut and marcu use a standard bottomup chart parsing algorithm to determine the discourse structure of sentences---more than half of the two-noun types in the bnc occur exactly once | 0 |
support vector machines have been shown to outperform other existing methods in text categorization---as noted in joachims , support vector machines are well suited for text categorisation | 1 |
we use glove word embeddings , which are 50-dimension word vectors trained with a crawled large corpus with 840 billion tokens---we use glove 300-dimension embedding vectors pre-trained on 840 billion tokens of web data | 1 |
this has shown to be effective for numerous nlp tasks as it can capture word morphology and reduce out-of-vocabulary---multi-task learning was shown to be effective for a variety of nlp tasks , such as pos tagging , chunking , named entity recognition or sentence compression | 1 |
for this model , we use a binary logistic regression classifier implemented in the lib-linear package , coupled with the ovo scheme---we train a linear support vector machine classifier using the efficient liblinear package | 1 |
shen et al proposed a string-to-dependency model , which restricted the target-side of a rule by dependency structures---shen et al proposed a string-to-dependency target language model to capture long distance word orders | 1 |
in addition , it is presupposed that a single semantic role is assigned to each syntactic argument---it is presupposed that a single semantic role is assigned to each syntactic argument | 1 |
katakana writing is a syllabary rather than an alphabet -- there is one symbol for ga ( ~ ) , another for gi ( 4~ ) , another for gu ( y ) , etc---katakana writing is a syllabary rather than an alphabet -- there is one symbol for ga ( ~i ) , another for gi ( 4e ) , another for gu ( p ' ) , etc | 1 |
motivated by psycholinguistic findings , we are currently investigating the role of eye gaze in spoken language understanding for multimodal conversational systems---motivated by these psycholinguistic findings , we are currently investigating the role of eye gaze in spoken language understanding | 1 |
specifically , our model employs the long short-term memory variant of rnn , which uses numbers of gates to address the problem of vanishing or exploding gradient when trained with back-propagation through time---in the training data , we found that 50 . 98 % sentences labeled as ¡° should be extracted ¡± belongs to th... | 0 |
in order to evaluate the quality of locating the wrong term translation , we applied the terminology verification service to an smt model trained with moses on the europarl corpus---we measure the bll performance using the standard top 1 accuracy metric | 0 |
sentiment analysis is the task in natural language processing ( nlp ) that deals with classifying opinions according to the polarity of the sentiment they express---these models can be tuned using minimum error rate training | 0 |
results are reported on two standard metrics , nist and bleu , on lower-cased data---we trained word embeddings using word2vec on 4 corpora of different sizes and types | 0 |
hierarchical phrase-based translation is one of the current promising approaches to statistical machine translation---the hierarchical phrase-based translation model has been widely adopted in statistical machine translation tasks | 1 |
we train skip-gram word embeddings with the word2vec toolkit 1 on a large amount of twitter text data---for the textual sources , we populate word embeddings from the google word2vec embeddings trained on roughly 100 billion words from google news | 1 |
table 4 : the feature set for product attribute extraction---for the final product attribute extraction | 1 |
we use the moses toolkit to train our phrase-based smt models---we used moses as the implementation of the baseline smt systems | 1 |
psl is a new model of statistical relation learning and has been quickly applied to solve many nlp and other machine learning tasks in recent years---psl is a new statistical relational learning method that has been applied to many nlp and other machine learning tasks in recent years | 1 |
we used the google news pretrained word2vec word embeddings for our model---we trained word embeddings using word2vec on 4 corpora of different sizes and types | 1 |
multi-document summarization on written text has been studied for over a decade---multi-document summarization on written documents has been studied for more than a decade | 1 |
we use the adam optimizer and mini-batch gradient to solve this optimization problem---the log-lineal combination weights were optimized using mert | 0 |
the performance of the phrase-based smt system is measured by bleu score and ter---the language models are 4-grams with modified kneser-ney smoothing which have been trained with the srilm toolkit | 0 |
bannard and callison-burch introduced the pivot approach to extracting paraphrase phrases from bilingual parallel corpora---the german-to-english baseline phrasebased system was trained on the europarl v7 corpus | 0 |
we used kneser-ney smoothing for training bigram language models---finally , we used kenlm to create a trigram language model with kneser-ney smoothing on that data | 1 |
when the assumptions of these models are violated , the power to detect significant geolinguistic associations is diminished---in which the geolinguistic dependence is obscured by noise , this can dramatically diminish the power of the test | 1 |
stance detection has been defined as automatically detecting whether the author of a piece of text is in favor of the given target or against it---we are able to get a ceafe score within 5 % of a non-streaming system while using only 30 % of the memory | 0 |
we focus on the 1000-example setting of subtask 1 : given a lemma with its part of speech and target morphological tags , generate the target inflected form---on universal morphological reinflection , subtask 1 : given a lemma and target morphological tags , generate the target inflected form | 1 |
a number of computational approaches for sentiment polarity classification of metaphorical language have also been proposed---to address this problem , we propose a novel joint learning algorithm that allows the feedbacks to be propagated from the classifiers for latter labels to the classifier | 0 |
we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus---we used trigram language models with interpolated kneser-kney discounting trained using the sri language modeling toolkit | 1 |
table 4 shows end-to-end translation bleu score results---the results evaluated by bleu score is shown in table 2 | 1 |
we use stanford corenlp to dependency parse sentences and extract the subjects and objects of verbs---zoph et al train a parent model on a highresource language pair in order to improve low-resource language pairs | 0 |
in the field of brain and neuroscience , analyzing semantic activities occurring in the human brain is an area of active study---activity based on language representations , such as the semantic categories of words , have been actively studied in the field of brain and neuroscience | 1 |
this metric corresponds to the hwc metric presented by liu and gildea---this metric corresponds to the stm metric presented by liu and gildea | 1 |
our cdsm feature is based on word vectors derived using a skip-gram model---we introduce the first global recursive neural parsing approach with optimality guarantees for decoding | 0 |
mikolov et al proposed a computationally efficient method for learning distributed word representation such that words with similar meanings will map to similar vectors---in this paper , we attempted to define a measure of distributional semantic content | 0 |
turian et al learned a crf model using word embeddings as input features for ner and chunking tasks---turian et al used unsupervised word representations as extra word features to improve the accuracy of both ner and chunking | 1 |
all annotations were carried out with the brat rapid annotation tool---annotation was conducted on a modified version of the brat web-based annotation tool | 1 |
by combining word alignments in two directions using heuristics , a single set of static word alignments is then formed---by combining word alignments in two directions using heuristics , a single set of static word alignments was then formed | 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---named entity recognition ( ner ) is the first step for many tasks in the fields of natural language processing and information retrieval | 0 |
all the language models are 5-grams with modified kneser-ney smoothing trained with kenlm---an english 5-gram language model is trained using kenlm on the gigaword corpus | 1 |
by increasing the penalty threshold the accuracy rises to 93.5 % , and with a single addition to the lexicon it reaches 98.0 %---by making the parser slightly more robust , the accuracy of the system rises to 93 . 5 % , and by adding one single word to the lexicon , the accuracy is boosted to 98 . 0 % | 1 |
this is illustrated in figure 1 , where we denote the centers of the gaussian distributions as concept vectors---this is illustrated in figure 5 , where i is a number representing salience ( section 3 shows how salience indices are derived ) | 1 |
we used a 4-gram language model which was trained on the xinhua section of the english gigaword corpus using the srilm 4 toolkit with modified kneser-ney smoothing---for the semantic language model , we used the srilm package and trained a tri-gram language model with the default goodturing smoothing | 1 |
ftd is typically diagnosed on the basis of the clinical observation of disorganized speech---several structure-based learning algorithms have been proposed so far | 0 |
we use the 300-dimensional pre-trained word2vec 3 word embeddings and compare the performance with that of glove 4 embeddings---meanwhile , we adopt glove pre-trained word embeddings 5 to initialize the representation of input tokens | 1 |
our system was one of the top performing systems---and was one of the top performing systems | 1 |
this paper addresses the development and evaluation of pronunciation features for an automated system for scoring spontaneous speech---we used the penn treebank to perform empirical experiments on the proposed parsing models | 0 |
we train the model using the adam optimizer with the default hyper parameters---we use binary crossentropy loss and the adam optimizer for training the nil-detection models | 1 |
we later applied the same basic methodology to dialogue act classification over one-on-one live chat data with provided message dependencies , demonstrating the generalisability of the original method---they later applied the same basic methodology to dialogue act classification over one-on-one live chat data with prov... | 1 |
yu and dredze combine cbow with word relations extracted from wordnet and ppdb---yu and dredze extend the cbow objective with synonymy constraints from wordnet and paraphrase database | 1 |
the feature weights of the translation system are tuned with the standard minimum-error-ratetraining to maximize the systems bleu score on the development set---all the feature weights and the weight for each probability factor are tuned on the development set with minimum-error-rate training | 1 |
the results show that the rule-based chunk approach is superior---as table 4 shows , the rule-based method outperformed the corpus-based method | 1 |
coreference resolution is a well known clustering task in natural language processing---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 | 1 |
empirical evaluation on the ace 2004 data set shows that the proposed method substantially improves over two baseline methods---experiment results on the ace 2004 data show that the multi-task transfer learning method achieves the best performance | 1 |
named entity recognition ( ner ) is the task of finding rigid designators as they appear in free text and classifying them into coarse categories such as person or location ( cite-p-24-4-6 )---named entity recognition ( ner ) is a fundamental task in text mining and natural language understanding | 1 |
in our extension of lcseg , we use a similar method to consolidate different segments ; however , in our case the linearity constraint is absent---lastly , we populate the adjacency with a distributional similarity measure based on word2vec | 0 |
the current state-of-the-art model , nsc ( cite-p-15-1-2 ) , introduced an attention mechanism called upa which is based on user and product information and applied this to a hierarchical lstm---nsc ( cite-p-15-1-2 ) is the current state-of-the-art model that utilizes a hierarchical lstm model ( cite-p-15-3-19 ) and in... | 1 |
however in practice it is always superior to earley 's parser since the prediction steps have been compiled before runtime---we use a tree-lstm in our parser to model the sub-trees during parsing | 0 |
we use the word2vec skip-gram model to train our word embeddings---socher et al use recursive auto-encoders for sentiment analysis on the sentence level | 0 |
thus scope information does not have to be completely determined---zhang et al explore different markov chain orderings for an n-gram model on mtus in rescoring | 0 |
blitzer et al proposed a structural correspondence learning method for domain adaptation and applied it to part-of-speech tagging---the benchmark model for topic modelling is latent dirichlet allocation , a latent variable model of documents | 0 |
additionally , we compile the model using the adamax optimizer---we update the gradient with adaptive moment estimation | 1 |
sentiment analysis is a collection of methods and algorithms used to infer and measure affection expressed by a writer---in a different vein , cite-p-19-1-12 introduced three unsupervised methods drawn from visual properties of images | 0 |
word sense disambiguation ( wsd ) is a key enabling-technology---word sense disambiguation ( wsd ) is the problem of assigning a sense to an ambiguous word , using its context | 1 |
for language model , we use a trigram language model trained with the srilm toolkit on the english side of the training corpus---we use sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus | 1 |
in this paper , we propose a novel and effective approach to sentiment analysis on product reviews---for each phrase pair , we use the sentence containing the phrase in source language | 0 |
additionally , we adapt our models to a new sms-chat domain and obtain a similar gain of 1.0 t er / 0.5 b leu points---with out-of-domain data , we obtain a similar boost of 1 . 0 t er / 0 . 5 b leu points over a strong domain-adapted sms-chat baseline | 1 |
we measure translation performance by the bleu and meteor scores with multiple translation references---for systems evaluation , we also use bleu score through the scripts at moses | 1 |
to further enhance the model performance , we use byte pair encoding with a coding size of 40k to segment the sentences of the training data into subwords---this syntactic information is obtained from the stanford parser | 0 |
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