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for the semantic language model , we used the srilm package and trained a tri-gram language model with the default goodturing smoothing---we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing | 1 |
efficiency of such learning method may suffer from the mismatch of dialogue state distribution between offline training and online interactive learning stages---with such pre-training approach is that the model may suffer from the mismatch of dialogue state distributions between supervised training and interactive learning stages | 1 |
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 way of using the characteristics of the language to cause a word , a sentence or a discourse to involve two or more different meanings | 1 |
we use distributed word vectors trained on the wikipedia corpus using the word2vec algorithm---we use the pre-trained word2vec embeddings provided by mikolov et al as model input | 1 |
we trained a trigram language model on the chinese side , with the srilm toolkit , using the modified kneser-ney smoothing option---we train a 4-gram language model on the xinhua portion of the gigaword corpus using the sri language toolkit with modified kneser-ney smoothing | 1 |
our system is based on the phrase-based part of the statistical machine translation system moses---for our experiments , we use a phrase-based translation system similar to moses | 1 |
we used 100 dimensional glove embeddings for this purpose---we used the 200-dimensional word vectors for twitter produced by glove | 1 |
blitzer et al apply structural correspondence learning for learning pivot features to increase accuracy in the target domain---coreference resolution is the problem of identifying which mentions ( i.e. , noun phrases ) refer to which real-world entities | 0 |
the first relies on google translator , the second is based on dbpedia , a structured version of wikipedia---which exploits google translator , the latter is based on a parallel corpus approach which relies on wikipedia | 1 |
previous work has reported the usefulness of salience for anaphora resolution---previous works reported the usefulness of salience for anaphora resolution | 1 |
socher et al learned compositional vector representations of sentences with a recursive neural network---we preprocessed the corpus with tokenization and true-casing tools from the moses toolkit | 0 |
in particular , we do not need a tokenizer to segment text in each of the input languages---we use the moses smt toolkit to test the augmented datasets | 0 |
the transformer is an encoder-decoder architecture which fully relies on attention---the transformer network , like most of the sequence-to-sequence models , follows an encoder-decoder architecture | 1 |
in recent years , there are various competitions devoted to grammar error correction , such as the hoo-2011 , hoo-2012 , dale et al , 2012 and the conll-2013 shared task---several shared tasks on grammatical error correction in english have been organized in recent years , including hoo 2011 , hoo 2012 , dale et al , 2012 and conll 2013 , ng et al , 2013 | 1 |
choi et al examine opinion holder extraction using crfs with several manually defined linguistic features and automatically learnt surface patterns---mikolov et al introduce a translation matrix for aligning embeddings spaces in different languages and show how this is useful for machine translation purposes | 0 |
we proposed a novel framework for modeling relational knowledge in word embeddings using rank-1 subspace regularization---we propose a novel approach to model relational knowledge based on low-rank subspace regularization | 1 |
we use a recurrent neural network with lstm cells to avoid the vanishing gradient problem when training long sequences---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 | 1 |
in model 4 , the variables in and percent are treated as influencing the values of rate , short , and pursue in order to achieve an ordering of variables as described above---in model 4 , the variables in and percent are treated as influencing the values of rate , short , and pursue in order to achieve an ordering of variables | 1 |
a pun is the exploitation of the various meanings of a word or words with phonetic similarity but different meanings---a * algorithm can be several times or orders of magnitude faster than the state-of-the-art k-best decoding algorithm | 0 |
for word-level embedding e w , we utilize pre-trained , 300-dimensional embedding vectors from glove 6b---for sentences , we tokenize each sentence by stanford corenlp and use the 300-d word embeddings from glove to initialize the models | 1 |
xiao et al propose a topic similarity model which incorporates the rule-topic distributions on both the source and target side into a hierarchical phrase-based system for rule selection---xiao et al present a topic similarity model based on lda that produces a feature that weights grammar rules based on topic compatibility | 1 |
as our final set of baselines , we extend two simple techniques proposed by mitchell and lapata that use element-wise addition and multiplication operators to perform composition---as our final set of baselines , we extend two simple techniques proposed by that use element-wise addition and multiplication operators to perform composition | 1 |
this paper introduced the problem of modelling frequency profiles of rumours in social media---this paper considers the problem of modelling temporal frequency profiles of rumours | 1 |
we adopted the case-insensitive bleu-4 as the evaluation metric---for evaluation metric , we used bleu at the character level | 1 |
crfs were first introduced by lafferty et al and have been successfully used for many nlp tagging tasks such as named entity recognition and shallow parsing---crfs have been shown to perform well on a number of nlp problems such as shallow parsing , table extraction , and named entity recognition | 1 |
in this paper , we present a multi-task learning method to improve implicit discourse relation classification by leveraging synthetic implicit discourse data---in this paper , we propose a multi-task learning based method to improve the performance of implicit discourse relation recognition ( as main task | 1 |
the various smt systems are evaluated using the bleu score---the bleu-4 metric implemented by nltk is used for quantitative evaluation | 1 |
the language model is a trigram-based backoff language model with kneser-ney smoothing , computed using srilm and trained on the same training data as the translation model---our baseline is a standard phrase-based smt system | 0 |
the experiments of the phrase-based smt systems are carried out using the open source moses toolkit---the statistical phrase-based systems were trained using the moses toolkit with mert tuning | 1 |
the srilm toolkit was used to build the 5-gram language model---the language model is trained and applied with the srilm toolkit | 1 |
we have used a bengali news corpus developed from the webarchives of a widely read bengali newspaper---we have used a bengali news corpus , developed from the web-archive of a widely read bengali newspaper for ner | 1 |
figure 1 : percent of postnominal simple ( green ) and heavy ( red ) adjectives across seventeen languages---we compared the performances of the systems using two automatic mt evaluation metrics , the sentence-level bleu score 3 and the document-level bleu score | 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---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 ) | 0 |
the smt systems are tuned on the dev development set with minimum error rate training using bleu accuracy measure as the optimization criterion---the feature weights for each system were tuned on development sets using the moses implementation of minimum error rate training | 1 |
in this paper , as a first step into this research , we explore different pattern representations , various existing pattern ranking approaches and some word similarity measures---in this paper , we have presented our research proposal , aiming at determining the impact of employing word similarity measures within pattern ranking approaches | 1 |
we applied a 5-gram mixture language model with each sub-model trained on one fifth of the monolingual corpus with kneser-ney smoothing using srilm toolkit---we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
the word analogy task is introduced by mikolov et al to quantitatively evaluate the linguistic regularities between pairs of word representations---the feature weights are tuned to optimize bleu using the minimum error rate training algorithm | 0 |
we use srilm toolkits to train two 4-gram language models on the filtered english blog authorship corpus and the xinhua portion of gigaword corpus , respectively---we also use 200 million words from ldc arabic gigaword corpus to generate a 5-gram language model using srilm toolkit , stolcke , 2002 translation to be our source in each case | 1 |
we present a new , sizeable dataset of noun– noun compounds with their syntactic analysis ( bracketing ) and semantic relations---we presented a new noun – noun compound dataset constructed from different linguistic resources , which includes bracketing information and semantic relations | 1 |
we use the stanford part of speech tagger to annotate each word with its pos tag---we used a manually created list of definitively positive and negative words and an automatically generated list of words and their associated sentiment polarities in the sentiment140 lexicon | 0 |
hatzivassiloglou and mckeown were the first to explore automatically learning the polarity of words from corpora---hatzivassiloglou and mckeown did the first work to tackle the problem for adjectives using a corpus | 1 |
a concept is defined by its attributes which consist of two parts : role and value restriction---synchronous context-free grammars are now widely used in statistical machine translation , with hiero as the preeminent example | 0 |
a sentiment lexicon is a list of words and phrases , such as excellent , awful and not bad , each is being assigned with a positive or negative score reflecting its sentiment polarity---a sentiment lexicon is a list of words and phrases , such as ” excellent ” , ” awful ” and ” not bad ” , each is being assigned with a positive or negative score reflecting its sentiment polarity and strength | 1 |
we use a 5-gram language model with modified kneser-ney smoothing , trained on the english side of set1 , as our baseline lm---we use srilm to train a 5-gram language model on the target side of our training corpus with modified kneser-ney discounting | 1 |
we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting---we used the srilm toolkit to build unpruned 5-gram models using interpolated modified kneser-ney smoothing | 1 |
for learning language models , we used srilm toolkit---for generating the translations from english into german , we used the statistical translation toolkit moses | 0 |
in this paper , we propose a novel instance-based evaluation framework for inference rules that takes advantage of crowdsourcing---we used the srilm toolkit to build unpruned 5-gram models using interpolated modified kneser-ney smoothing | 0 |
we then obtain the bleu and meteor translation scores---we also report the results using bleu and ter metrics | 1 |
text simplification ( ts ) is the task of modifying an original text into a simpler version of it---text simplification ( ts ) is generally defined as the conversion of a sentence into one or more simpler sentences | 1 |
we obtained a phrase table out of this data using the moses toolkit---in philosophy and linguistics , it is accepted that negation conveys positive meaning | 0 |
the weights of the different feature functions were optimised by means of 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 |
sentiment analysis is the task in natural language processing ( nlp ) that deals with classifying opinions according to the polarity of the sentiment they express---one of the first challenges in sentiment analysis is the vast lexical diversity of subjective language | 1 |
we used the svm implementation of scikit learn---we used standard classifiers available in scikit-learn package | 1 |
teachers report that it is feasible to integrate into their curriculum---teachers indicate that it is feasible to incorporate the intervention into their curriculum | 1 |
the code and data used in this paper is available at http : //rtw.ml.cmu.edu/emnlp2015 sfe/---our mt system is a phrase-based , that is developed using the moses statistical machine translation toolkit | 0 |
we score candidate terms according to a word2vec and tf-idf ranking measure---we initialize our word vectors with 300-dimensional word2vec word embeddings | 1 |
the nnlm weights are optimized as the other feature weights using minimum error rate training---feature weights are tuned using minimum error rate training on the 455 provided references | 1 |
sentiment classification is a very domain-specific problem ; training a classifier using the data from one domain may fail when testing against data from another---the relation > is the transitive closure of r | 0 |
the anaphor is a pronoun and the referent is in the cache ( in focus )---the anaphor is a pronoun and the referent is in operating memory ( not in focus ) | 1 |
language identification is a well-studied problem ( cite-p-20-3-2 ) , but it is typically only studied in its canonical text-classification formulation , identifying a document ’ s language given sample texts from a few different languages---language identification is the task of identifying the language a given document is written in | 1 |
inkpen and hirst apply gloss-and context vectors to the disambiguation of near-synonyms in dictionary entries---word embeddings can be directly used for solving intrinsic tasks like word similarity and word analogy | 0 |
we used the moses toolkit to build an english-hindi statistical machine translation system---we conducted baseline experiments for phrasebased machine translation using the moses toolkit | 1 |
to obtain our results , we have used a novel proof technique that exploits an already known construction for the renormalization of probabilistic context-free grammars---we prove some properties of a renormalization technique for probabilistic context-free grammars , and use this property to show our main results | 1 |
sentiment analysis is the task in natural language processing ( nlp ) that deals with classifying opinions according to the polarity of the sentiment they express---sentiment analysis is a recent attempt to deal with evaluative aspects of text | 1 |
we employ conditional random fields to predict the sentiment label for each segment---we solve this sequence tagging problem using the mallet implementation of conditional random fields | 1 |
hochreiter and schmidhuber developed long short-term memory to overcome the long term dependency problem---long short-term memory was introduced by hochreiter and schmidhuber to overcome the issue of vanishing gradients in the vanilla recurrent neural networks | 1 |
linguistica and morfessor are built around an idea of optimally encoding the data , in the sense of minimal description length---novelty of this task lies in the fact that a model built using only twitter data is used to classify instances from other short text | 0 |
relation extraction ( re ) is the task of assigning a semantic relationship between a pair of arguments---relation extraction is the task of finding semantic relations between entities from text | 1 |
the model was built using the srilm toolkit with backoff and kneser-ney smoothing---language models were built using the srilm toolkit 16 | 1 |
we have shown co-training to be a promising approach for predicting emotions with spoken dialogue data---we investigate the automatic labeling of spoken dialogue data , in order to train a classifier that predicts students ’ emotional states | 1 |
coreference resolution is a field in which major progress has been made in the last decade---coreference resolution is a key task in natural language processing ( cite-p-13-1-8 ) aiming to detect the referential expressions ( mentions ) in a text that point to the same entity | 1 |
mccrae et al propose lemon , a conceptual model for lexicalizing ontologies as an extension of the lexinfo model---buitelaar et al describe lexinfo , an lmf-model that is used for lexicalizing ontologies | 1 |
the model parameters are trained using minimum error-rate training---the weights for these features are optimized using mert | 1 |
as wiktionary contains all parts of speech and our method is independent of word frequency , neither limitation applies to this work---and our method is independent of word frequency , neither limitation applies to this work | 1 |
for example , blitzer et al proposed to compute the correspondence among features from different domains using their associations with pivot features based on structural correspondence learning---in this paper , we have also discussed some important implications of the notion of critical tokenization in the area of character string tokenization | 0 |
as a grammar development system , gf is comparable to regulus , lkb , and xle---in these respects it is quite similar to the lkb parser-generator system | 1 |
conditional random fields are discriminatively-trained undirected graphical models that find the globally optimal labeling for a given configuration of random variables---based on the findings , we define a syntactic type system for the time expression , and propose a type-based time expression | 0 |
in , a crf sequence modeling approach was used for normalizing deletion-based abbreviation---pennell and liu used a crf sequence modeling approach for deletion-based abbreviations | 1 |
each task is based on a database schema which defines the domain of interest---since each task involves a separate schema and database of entities | 1 |
han et al proposed a dataset and evaluation setup for few-shot relation classification which assumes access to full supervision for training relations---in addition , the setup in han et al requires a model architecture specific to few-shot learning based on distance metric learning | 1 |
in particular , neelakantan et al described a modified skipgram algorithm that clusters instances on the fly , effectively training several vectors per word---exactly , it is a usual practice to resort to approximate search / decoding algorithms | 0 |
in this paper , we propose a novel discriminative language model , which can be applied quite generally---in aggregate , these constraints can greatly improve the consistency over the overall document-level predictions | 0 |
according to pickering and garrod , the act of engaging in a dialog facilitates the use of similar representations at all linguistic levels , and these representations are shared between speech production and comprehension processes---as for ej translation , we use the stanford parser to obtain english abstraction trees | 0 |
coreference resolution is the process of linking together multiple expressions of a given entity---coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world | 1 |
we show a relative reduction of alignment error rate of about 38 %---we have shown a relative reduction of aer of about 38 % | 1 |
we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting---we present a data-driven approach to learn user-adaptive referring expression generation ( reg ) | 0 |
the bleu is a classical automatic evaluation method for the translation quality of an mt system---bleu is a system for automatic evaluation of machine translation | 1 |
semantic parsing is the task of transducing natural language ( nl ) utterances into formal meaning representations ( mrs ) , commonly represented as tree structures---semantic parsing is the problem of mapping natural language strings into meaning representations | 1 |
coreference resolution is a set partitioning problem in which each resulting partition refers to an entity---although coreference resolution is a subproblem of natural language understanding , coreference resolution evaluation metrics have predominately been discussed in terms of abstract entities and hypothetical system errors | 1 |
we used a 5-gram language model with modified kneser-ney smoothing , built with the srilm toolkit---we trained a standard 5-gram language model with modified kneser-ney smoothing using the kenlm toolkit on 4 billion running words | 1 |
psl is a probabilistic logic framework designed to have efficient inference---soft logic ( psl ) is a recently developed framework for probabilistic logic | 1 |
we evaluated the reordering approach within the moses phrase-based smt system---we use the moses smt toolkit to test the augmented datasets | 1 |
however , we use a large 4-gram lm with modified kneser-ney smoothing , trained with the srilm toolkit , stolcke , 2002 and ldc english gigaword corpora---for the language model we use the corpus of 60,000 simple english wikipedia articles 3 and build a 3-gram language model with kneser-ney smoothing trained with srilm | 1 |
we initialize the word embeddings for our deep learning architecture with the 100-dimensional glove vectors---we use glove word embeddings , an unsupervised learning algorithm for obtaining vector representations of words | 1 |
the nonembeddings weights are initialized using xavier initialization---all the weights are initialized with xavier initialization method | 1 |
mikolov et al proposed a computationally efficient method for learning distributed word representation such that words with similar meanings will map to similar vectors---bengio and mikolov proposed algorithms to train word embeddings by maximizing the probability of a word given by the previous word | 1 |
semantic parsing is the task of translating natural language utterances into a machine-interpretable meaning representation ( mr )---semantic parsing is a domain-dependent process by nature , as its output is defined over a set of domain symbols | 1 |
we implemented the different aes models using scikit-learn---we use scikit learn python machine learning library for implementing these models | 1 |
sentence compression is the task of compressing long sentences into short and concise ones by deleting words---sentence compression is the task of compressing long , verbose sentences into short , concise ones | 1 |
for word-level embedding e w , we utilize pre-trained , 300-dimensional embedding vectors from glove 6b---for the classification task , we use pre-trained glove embedding vectors as lexical features | 1 |
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