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word sense disambiguation ( wsd ) is a particular problem of computational linguistics which consists in determining the correct sense for a given ambiguous word---framenet is a comprehensive lexical database that lists descriptions of words in the frame-semantic paradigm | 0 |
we could also do more than simply use the sentences and paragraphs as their own definitions---we use the logistic regression implementation of liblinear wrapped by the scikit-learn library | 0 |
to have a more insightful evaluation , we design three experiments with three different evaluation metrics---into the model , we design three kinds of experiments together with three different evaluation metrics | 1 |
to construct the local embeddings , we use two neural network architectures introduced by mikolov et al on our corpus , namely , the cbow and the skip-gram architectures shown in figure 1---to train the link embeddings , we use the speedy , skip-gram neural language model of mikolov et al via their toolkit word2vec | 1 |
we use the word2vec framework in the gensim implementation to generate the embedding spaces---semantic parsing is the task of mapping natural language to a formal meaning representation | 0 |
nevertheless , it is clear that it results in error accumulation and suffers from its inability to correct mistakes in previous stages---and is known to suffer from error accumulation and an inability to correct mistakes in previous stages | 1 |
it can seem to be a very hard problem or one that is somewhat easier---it can seem to be a very hard problem or one that is relatively easy | 1 |
results point out the limitations of purely term-based methods to this challenging task---but more importantly , this work highlights limitations of purely ir-based methods | 1 |
one of the popular statistical machine translation paradigms is the phrase-based model---phrase-based translation models are widely used in statistical machine translation | 1 |
twitter is a well-known social network service that allows users to post short 140 character status update which is called “ tweet ”---twitter is a microblogging site where people express themselves and react to content in real-time | 1 |
relation extraction is the task of detecting and classifying relationships between two entities from text---relation extraction is the task of predicting attributes and relations for entities in a sentence ( zelenko et al. , 2003 ; bunescu and mooney , 2005 ; guodong et al. , 2005 ) | 1 |
in all the experiments , we use the na ? ? ve bayes multinomial classifier and its weka implementation 2 , with term-frequencies as feature values---pantel and lin and erk and pad贸 attempt to include syntactic context in distributional models | 0 |
work on data-driven approaches has led to insights about the importance of linguistic features for sentence linearization decisions---work on data-driven approaches has led to insights into the importance of linguistic features for sentence linearisation decisions | 1 |
although a vocabulary-based language modeling approach outperformed the grammar-based predictor , an interpolated measure using confidence scores for the grammar-based predictions showed improvement over both individual measures---while the vocabulary-based language modeling approach outperformed the grammar-based approach , grammar-based predictions can be combined using confidence scores with the vocabulary-based predictions | 1 |
for representing words , we used 100 dimensional pre-trained glove embeddings---in order to initialize the words in the triplets , we used 300 dimensional glove embedding | 1 |
for example , sentences such as “ bake for 50 minutes ” do not explicitly mention what to bake or where---for example , sentences such as “ bake for 50 minutes ” do not explicitly mention what to bake | 1 |
we find that the learner ’ s uncertainty is a robust predictive criterion that can be easily applied to different learning models---the annotation scheme is based on an evolution of stanford dependencies and google universal part-of-speech tags | 0 |
relation extraction is a traditional information extraction task which aims at detecting and classifying semantic relations between entities in text ( cite-p-10-1-18 )---we use case-sensitive bleu-4 to measure the quality of translation result | 0 |
srl is a complex task , which is reflected by the algorithms used to address it---srl is the task of identifying arguments for a certain predicate and labelling them | 1 |
we use nltk to get sentiment scores using the sentiwordnet corpus---for the newsgroups and sentiment datasets , we used stopwords from the nltk python package | 1 |
hassan and menezes proposed an approach based on the random walk algorithm on a contextual similarity bipartite graph , constructed from n-gram sequences on a large unlabeled text corpus---we trained kneser-ney discounted 5-gram language models on each available corpus using the srilm toolkit | 0 |
specifically , we design a generic shared-private learning framework to model the text sequence---in this paper , we propose an adversarial multi-task learning framework , alleviating the shared and private | 1 |
socher et al learned compositional vector representations of sentences with a recursive neural network---socher et al used an rnn-based architecture to generate compositional vector representations of sentences | 1 |
the standard approach to word alignment from sentence-aligned bitexts has been to construct models which generate sentences of one language from the other , then fitting those generative models with em---the standard approach to word alignment is to construct directional generative models , which produce a sentence in one language given the sentence in another language | 1 |
we used the moses toolkit to build an english-hindi statistical machine translation system---we used moses software to extract lexical translations by aligning the dataset of 5,000 sm-im pairs | 1 |
we used yamcha 1 , which is a general purpose svm-based chunker---discourse parsing is the process of discovering the latent relational structure of a long form piece of text and remains a significant open challenge | 0 |
we use the publicly available 300-dimensional word vectors of mikolov et al , trained on part of the google news dataset---we use the pre-trained word2vec embeddings provided by mikolov et al as model input | 1 |
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 processing community---dependency parsing is a fundamental task for language processing which has been investigated for decades | 1 |
our models are quite similar to this model but we used different variety of rnn in place of window based neural network---lexical features only , acoustic and prosodic features only , or a combination of both | 0 |
this paper proposes the hierarchical directed acyclic graph ( hdag ) kernel---this paper proposes the ¡° hierarchical directed acyclic graph ( hdag ) kernel | 1 |
lexical chains are used to link semanticallyrelated words and phrases---part-of-speech ( pos ) tagging is a critical task for natural language processing ( nlp ) applications , providing lexical syntactic information | 0 |
to prevent over-fitting we make use of dropout layers at the summary embeddings and the output softmax layer---to prevent overfitting , we apply dropout operators to non-recurrent connections between lstm layers | 1 |
furthermore , we propose a way to generate onthe-fly knowledge in logical inference , by combining our framework with the idea of tree transformation---the model parameters in word embedding are pretrained using glove | 0 |
in this paper , we present a hybrid approach for performing token and sentence levels dialect identification in arabic---in this paper , we present a hybrid approach for performing token and sentence levels | 1 |
semantic parsing is the problem of translating human language into computer language , and therefore is at the heart of natural language understanding---semantic parsing is the task of translating text to a formal meaning representation such as logical forms or structured queries | 1 |
the pad贸 dataset includes 18 verbs as well as up to twelve nominal arguments , totalling 207 verb-noun pairs---one such dataset by pad贸 includes 18 verbs with up to 12 candidate nominal arguments and totals 414 verb-nounrole triples | 1 |
in recent times , the creation and expansion of these resources has been increasingly shifting into an automated and/or interactive system facilitated task---creation and expansion has increasingly been shifting towards an automated and / or interactive system facilitated task | 1 |
we use the pre-trained glove 50-dimensional word embeddings to represent words found in the glove dataset---we use glove word embeddings , an unsupervised learning algorithm for obtaining vector representations of words | 1 |
the combination of transcripts and acoustic features has also provided good results for dialect identification---methods suffer from data sparsity problem when they are conducted on short and informal texts , especially microblog messages | 0 |
we use 300d glove vectors trained on 840b tokens as the word embedding input to the lstm---we use the pre-trained glove 50-dimensional word embeddings to represent words found in the glove dataset | 1 |
in this work , we tackle the task of machine translation ( mt ) without parallel training data---in this paper , we address the problem of learning a full translation model from non-parallel data | 1 |
and the svm model is effective for the personal detailed information extraction---with this proposed cascaded framework improves the average f-score of detailed information extraction | 1 |
pitler and nenkova show that discourse coherence features are more informative than other features for ranking texts with respect to their readability---hearst extracted information from lexico-syntactic expressions that explicitly indicate hyponymic relationships | 0 |
the srilm toolkit was used to build this language model---trigram language models are implemented using the srilm toolkit | 1 |
to examine the performance of proposed method , we conduct an extensive experiment on two commonly used datasets , i.e. , newsgroup and industry sector---effectiveness and robustness of proposed method , we conduct an extensive experiment on two commonly used corpora , i . e . , industry sector and newsgroup | 1 |
we obtained both phrase structures and dependency relations for every sentence using the stanford parser---we pre-processed the data to add part-ofspeech tags and dependencies between words using the stanford parser | 1 |
first , we suggest a novel set of entailment indicators that help to detect the likelihood of verb entailment---to this end , we propose a much richer novel set of linguistically motivated cues for detecting entailment between verbs | 1 |
sentence compression is a paraphrasing task where the goal is to generate sentences shorter than given while preserving the essential content---we use srilm to train a 5-gram language model on the target side of our training corpus with modified kneser-ney discounting | 0 |
we use the log-linear model proposed by och and ney for statistical machine translation and analogous transliteration features---we integrate the probabilistic list of translation options into the phrase-based decoder using the standard log-linear approach | 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---sentiment analysis is the task of identifying positive and negative opinions , sentiments , emotions and attitudes expressed in text | 0 |
in addition to the ie tasks in the biomedical domain , negation scope learning has attracted increasing attention in some natural language processing tasks , such as sentiment classification---in addition to the ie tasks in the biomedical domain , son learning has attracted more and more attention in some natural language processing tasks , such as sentiment classification | 1 |
in the final two articles , by piotrovskij and marc ? uk , the authors strongly advocate what they consider to be practical approaches to mt , while dismissing much of the work cited in the first three articles as misguided and counterproductive---in the final two articles , by piotrovskij and marcuk , the authors strongly advocate what they consider to be practical approaches to mt , while dismissing much of the work cited in the first three articles | 1 |
we used the srilm software 4 to build langauge models as well as to calculate cross-entropy based features---we previously proposed a data-modeling driven framework for rapid prototyping of sds | 0 |
sentiment analysis is a research area in the field of natural language processing---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 |
recurrent neural networks are widely used to learn a representation for a sequence of words for essay scoring---both recurrent neural networks and convolution neural networks have been used to automatically score input essays | 1 |
sentiment analysis is the task of identifying the polarity ( positive , negative or neutral ) of review---sentiment analysis is a technique to classify documents based on the polarity of opinion expressed by the author of the document ( cite-p-16-1-13 ) | 1 |
we use the multi-class logistic regression classifier from the liblinear package 2 for the prediction of edit scripts---we use the l2-regularized logistic regression of liblinear as our term candidate classifier | 1 |
we use srilm for n-gram language model training and hmm decoding---for probabilities , we trained 5-gram language models using srilm | 1 |
word embeddings represent each word as a low-dimensional vector where the similarity of vectors captures some aspect of semantic similarity of words---word embeddings are distributed vector presentations of words , capturing their syntactic and semantic information | 1 |
semantic role labeling is a research problem which finds in a given sentence the predicates and their arguments ( identification ) , and further labels the semantic relationship between predicates and arguments , that is , their semantic roles ( classification )---semantic role labeling is the task of determining the constituents of a sentence that represent semantic arguments with respect to a predicate and labeling each with a semantic role | 1 |
the circles denote fixations , and the lines are saccades---circles denote variable nodes , and squares denote factor nodes | 1 |
conditional random fields is a popular and efficient ml technique for supervised sequence labeling---conditional random fields are a popular family of models that have been proven to work well in a variety of sequence tagging nlp applications | 1 |
for the phrase based system , we use moses with its default settings---for decoding , we used moses with the default options | 1 |
relation extraction is the task of detecting and characterizing semantic relations between entities from free text---relation extraction is a fundamental task in information extraction | 1 |
we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing---the system used a tri-gram language model built from sri toolkit with modified kneser-ney interpolation smoothing technique | 1 |
zoph et al use transfer learning to improve nmt from low-resource languages into english---zoph et al train a parent model on a highresource language pair in order to improve low-resource language pairs | 1 |
for both translation directions , we trained nmt and smt systems , and combined them through n-best list reranking using several informative features---for both translation directions , we trained supervised neural mt and statistical mt systems , and combined them through n-best list reranking using different informative features as proposed by marie and fujita | 1 |
this parsing approach is very similar to the one used successfully by nivre et al , but we use a maximum entropy classifier to determine parser actions , which makes parsing considerably faster---this parsing approach is very similar to the one used successfully by nivre et al , but we use a maximum entropy classifier to determine parser actions , which makes parsing extremely fast | 1 |
we also compare our results to those obtained by running the system of durrett and denero on the same training and test data---we also compare our results to those obtained using the system of durrett and denero on the same test data | 1 |
we trained a continuous bag of words model of 400 dimensions and window size 5 with word2vec on the wiki set---we used word2vec , a powerful continuous bag-of-words model to train word similarity | 1 |
word segmentation is a prerequisite for many natural language processing ( nlp ) applications on those languages that have no explicit space between words , such as arabic , chinese and japanese---word segmentation is the first step prior to word alignment for building statistical machine translations ( smt ) on language pairs without explicit word boundaries such as chinese-english | 1 |
different from previous studies which only obtain word embeddings , our model can learn vector representations for both words and documents in bilingual texts---we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing | 0 |
the subtree ranking approach with a maximum entropy model significantly outperformed the other approaches---approach with a maximum entropy model significantly outperformed the other approaches | 1 |
experiments on benchmark demonstrate good performance of our model---and experimental results show the effectiveness of our model | 1 |
entity linking ( el ) is the task of automatically linking mentions of entities such as persons , locations , or organizations to their corresponding entry in a knowledge base ( kb )---entity linking ( el ) is the task of automatically linking mentions of entities ( e.g . persons , locations , organizations ) in a text to their corresponding entry in a given knowledge base ( kb ) , such as wikipedia or freebase | 1 |
in this paper , we presented a classification model towards the automation of mi coding using the miti coding system---the model uses non-negative matrix factorization in order to find latent dimensions | 0 |
we used srilm for training the 5-gram language model with interpolated modified kneser-ney discounting ,---for this language model , we built a trigram language model with kneser-ney smoothing using srilm from the same automatically segmented corpus | 1 |
chandar a p et al and zhou et al used the autoencoders to model the connections between bilingual sentences---zhou et al employed both unsupervised and supervised neural networks to learn bilingual sentiment word embedding | 1 |
experimental results show that our proposed ensemble methods are simple yet effective---experimental results reflect that our method is effective | 1 |
word embeddings are a crucial component in many nlp approaches since they capture latent semantics of words and thus allow models to better train and generalize---it is widely recognized that word embeddings are useful because both syntactic and semantic information of words are well encoded | 1 |
we extract named entities using a python wrapper for the stanford ner tool---in the above examples , classifier “ hiki ” is used to count noun “ inu ( dog ) ” | 0 |
we used moses to train an alignment model on the created paraphrase dataset---we used the moses toolkit to build mt systems using various alignments | 1 |
moses is used as a baseline phrase-based smt system---moses is used as the baseline phrase-based smt system | 1 |
text simplification ( ts ) is the task of modifying an original text into a simpler version of it---experiments on the benchmark data set show that our model achieves comparable and even better performance | 0 |
a pun is a form of wordplay in which one signifier ( e.g. , a word or phrase ) suggests two or more meanings by exploiting polysemy , or phonological similarity to another signifier , for an intended humorous or rhetorical effect---pun is a figure of speech that consists of a deliberate confusion of similar words or phrases for rhetorical effect , whether humorous or serious | 1 |
we employ a neural method , specifically the continuous bag-of-words model to learn high-quality vector representations for words---coreference resolution is the problem of identifying which mentions ( i.e. , noun phrases ) refer to which real-world entities | 0 |
in section 3 , we review related work in data-driven dialog modeling---in future work , we will address the use of data-driven dialog management | 1 |
arabic is a highly inflectional language with 85 % of words derived from trilateral roots ( alfedaghi and al-anzi 1989 )---moreover , arabic is a morphologically complex language | 1 |
specifically , we tested the methods word2vec using the gensim word2vec package and pretrained glove word embeddings---we initialize the embedding layer using embeddings from dedicated word embedding techniques word2vec and glove | 1 |
concept similarity techniques are mainly limited to the knowledge that their underlying lexical resources provide---techniques tend to base their computation on the knowledge obtained from various lexical resources | 1 |
the quality of machine translation systems have been significantly improved over the past few years , especially with the development of neural machine translation models---the advent of neural machine translation has led to remarkable improvements in machine translation quality but has also produced models that are much less interpretable | 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---table 1 shows statistics from sections 2-21 of the penn wsj treebank | 0 |
negation focus is defined as the special part in sentence , which is most prominently or explicitly negated by a negative expression---when performing community detection can improve the detection of sentiment-based communities | 0 |
we used srilm to build a 4-gram language model with interpolated kneser-ney discounting---we used kenlm with srilm to train a 5-gram language model based on all available target language training data | 1 |
for both languages , we used the srilm toolkit to train a 5-gram language model using all monolingual data provided---a 4-gram language model was trained on the monolingual data by the srilm toolkit | 0 |
zbib et al , 2012 ) used crowd sourcing to build levantineenglish and egyptian-english parallel data---zbib et al used crowdsourcing to build levantine-english and egyptian-english parallel corpora | 1 |
in section 2 , we discuss previous work , followed by an explanation of our model and its implementation in sections 3 and 4---barzilay and mckeown utilized multiple english translations of the same source text for paraphrase extraction | 0 |
janus is a natural language understanding and generation system that allows the user to interface with several knowledge bases maintained by the u.s. navy---janus is a natural language understanding and generation system which allows the user to interface with several knowledge bases maintained by the us navy | 1 |
the tokens are fed into an embedding layer which is initialized with glove word-embedding trained with a large twitter corpus---we first show that the previous neural network model of can be viewed as a coarse approximation to inference with isbns | 0 |
wang et al presented a syntactic tree matching method for finding similar questions---wang et al presents a method to retrieve similar questions that could be worth taking in consideration for the task | 1 |
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