text stringlengths 82 736 | label int64 0 1 |
|---|---|
luong and manning also propose an hybrid word-character model to handle the rare word problem---luong and manning have proposed a hybrid nmt model flexibly switching from the word-based to the character-based model | 1 |
we cast the problem of event property extraction as a sequence labeling task , using conditional random fields for learning and inference---to solve this dynamic state tracking problem , we propose a sequential labeling approach using linear-chain conditional random fields | 1 |
we use srilm for training a trigram language model on the english side of the training data---for both languages , we used the srilm toolkit to train a 5-gram language model using all monolingual data provided | 1 |
the use of voting mechanisms for integrating discrete modules is original---voting mechanisms are used for integrating discrete modules | 1 |
however , words can have different semantic meanings in different contexts---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 | 0 |
we also use a 4-gram language model trained using srilm with kneser-ney smoothing---we build an open-vocabulary language model with kneser-ney smoothing using the srilm toolkit | 0 |
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---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 |
an argument usually consists of a central claim ( or conclusion ) and several supporting premises---the argument , which is the target concept , is viewed in terms of a battle ( or a war ) , the source concept | 1 |
markov logic is a probabilistic extension of finite first-order logic---marlov logic is a combination of first-order logic and markov networks | 1 |
the translation systems were evaluated by bleu score---results were evaluated with both bleu and nist metrics | 1 |
the reader is referred to for a detailed description of the acoustic analysis procedure---for a detailed description of the system we have developed , the reader is referred to | 1 |
distributed representations of words have been widely used in many natural language processing tasks---word to vector algorithms , such as skip-gram and continuous bag of words , are widely used in natural language processing tasks | 1 |
for this , we extend the semantically conditioned long short-term memory network proposed by with surface features to control the manipulation of the surface realization---we use the semantically conditioned long shortterm memory network proposed by as our generator , which has a specialized cell to process the one-hot encoded mr-vector | 1 |
as a classifier , we choose a first-order conditional random field model---brown and levinson created a theory of politeness , articulating a set of strategies which people employ to demonstrate different levels of politeness | 0 |
since coreference resolution is a pervasive discourse phenomenon causing performance impediments in current ie systems , we considered a corpus of aligned english and romanian texts to identify coreferring expressions---coreference resolution is the task of determining which mentions in a text are used to refer to the same real-world entity | 1 |
for word-level embedding e w , we utilize pre-trained , 300-dimensional embedding vectors from glove 6b---for the word-embedding based classifier , we use the glove pre-trained word embeddings | 1 |
we present three different approaches for a careful comparison and analysis---this paper proposes the method about discovering sense boundary | 0 |
we use the automatic mt evaluation metrics bleu , meteor , and ter , to evaluate the absolute translation quality obtained---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 | 1 |
the lr and svm classifiers were implemented with scikit-learn---to measure the importance of the generated questions , we use lda to identify the important sub-topics from the given body of texts | 0 |
for evaluation , caseinsensitive nist bleu is used to measure translation performance---such features have been useful in a variety of english nlp models , including chunking , named entity recognition , and spoken language understanding | 0 |
coreference resolution is a well known clustering task in natural language processing---syntactic reordering approaches are an effective method for handling systematic differences in word order between source and target languages within the context of statistical machine translation ( smt ) systems | 0 |
for word-level embedding e w , we utilize pre-trained , 300-dimensional embedding vectors from glove 6b---to keep consistent , we initialize the embedding weight with pre-trained word embeddings | 1 |
we used the phrasebased translation system in moses 5 as a baseline smt system---we used a phrase-based smt model as implemented in the moses toolkit | 1 |
typically , shen et al propose a string-todependency model , which integrates the targetside well-formed dependency structure into translation rules---semantic role labeling ( srl ) is a kind of shallow sentence-level semantic analysis and is becoming a hot task in natural language processing | 0 |
coreference resolution is a task aimed at identifying phrases ( mentions ) referring to the same entity---coreference resolution is the process of linking together multiple expressions of a given entity | 1 |
in this paper , we investigate the problem of automatic domain partitioning---in this work , we propose an automatic domain partitioning approach that aims at providing better domain | 1 |
liu et al employed clustering to extract keywords that cover all important topics from the original text---liu et al propose to cluster candidate words based on their semantic relationship to ensure that the extracted keyphrases cover the entire document | 1 |
the minimum description length principle is about finding the optimal balance between the size of a model and the size of some data given the model---our 5-gram language model is trained by the sri language modeling toolkit | 0 |
in the future studies , we would explore the possibility of promoting diversity on the learning procedure , by directly optimizing diversity loss in the cost function---in the future studies , we would explore the possibility of promoting diversity on the learning procedure , by directly optimizing diversity loss | 1 |
thus , zesch and gurevych semi-automatically created word pairs from domain-specific corpora---zesch and gurevych created a third dataset from domain-specific corpora using a semi-automatic process | 1 |
in this work , we focus on extracting subtasks from a given collection of on-task search queries---in our current study , we propose a method for extracting search subtasks from a given collection of queries | 1 |
word sense disambiguation ( wsd ) is the task of determining the correct meaning for an ambiguous word from its context---the evaluation metric is case-sensitive bleu-4 | 0 |
riloff et al investigate sarcasm where the writer holds a positive sentiment toward a negative situation---riloff et al consider a positive verb used in a negative sentiment context to indicate sarcasm | 1 |
relation extraction is a fundamental task that enables a wide range of semantic applications from question answering ( cite-p-13-3-12 ) to fact checking ( cite-p-13-3-10 )---the feature weight 位 i in the log linear model is determined by using the minimum error rate training method | 0 |
blei et al showed that using lda for dimensionality reduction can improve performance for supervised text classification---blei et al proposed lda as a general bayesian framework and gave a variational model for learning topics from data | 1 |
in an evaluation on 826 argumentative essays , our learning-based approach , which combines our novel features with n-gram features and faulkner¡¯s features , significantly outperformed four baselines , including our reimplementation of faulkner¡¯s system---in an evaluation on 826 essays , our approach significantly outperforms four baselines , one of which relies on features previously developed specifically for stance classification | 1 |
to tackle the disadvantages of the supervised coherence model , guinaudeau and strube proposed a graph model to measure text coherence---guinaudeau and strube created an approach based on graph to eliminate the process of machine learning of the entity grid model from barzilay and lapata | 1 |
we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit---we used the srilm toolkit to train a 4-gram language model on the xinhua portion of the gigaword corpus , which contains 238m english words | 1 |
the word embeddings are pre-trained by skip-gram---the embeddings have been trained with word2vec on twitter data | 1 |
the srilm toolkit is used to train 5-gram language model---a 4-gram language model is trained on the monolingual data by srilm toolkit | 1 |
only two systems were presented for this subtask obtaining quite poor results ( f1 below 0,02 )---for this subtask obtaining quite poor results ( f1 below 0 , 02 ) | 1 |
twitter is a popular microblogging service , which , among other things , is used for knowledge sharing among friends and peers---twitter is a microblogging social network launched in 2006 with 310 million active users per month and where 340 million tweets are daily generated 1 | 1 |
for english we used tags that were obtained by enriching pos tags from treetagger with additional morphological features such as number for determiners---for english we used part-of-speech tags obtained using treetagger , enriched with more finegrained tags for the number of determiners , in order to target more agreement issues , since nouns already have number in the tagset | 1 |
entity type classification is the task of assigning type labels ( e.g. , person , location , organization ) to mentions of entities in documents---in this paper , we proposed multi-step stacked learning to extract n-gram features | 0 |
coreference resolution is a well known clustering task in natural language processing---however , in contrast to the original arc-eager parsing strategy , we use an arc-standard bottom-up algorithm , as described in nivre | 0 |
in they employed social relation for user-level sentiment analysis---tan , lee et al employed social relation for user-level sentiment analysis | 1 |
by treating time as a continuous variable , we can capture this gradual shift---in this paper , we discuss methods for automatically creating models of dialog structure using dialog | 0 |
medium timescale dependencies can be encoded in the dynamic of the network by using dynamic weights updated more slowly , a---while longer timescale dependencies are encoded in the dynamic of the lower-level network | 1 |
ccgs are a linguistically-motivated formalism for modeling a wide range of language phenomena---ccg is a linguistically-motivated categorial formalism for modeling a wide range of language phenomena | 1 |
the systems will be implemented using a discriminative , log-linear model , using the language and translation models as feature functions---in this paper , we integrate the context and glosses of the target word into a unified framework | 0 |
morphological analysis is the first step for most natural language processing applications---we assume that a morphological analysis consists of three processes : tokenization , dictionary lookup , and disambiguation | 1 |
we employed the machine learning tool of scikit-learn 3 , for training the classifier---we used scikit-learn library for all the machine learning models | 1 |
yih et al use an array of lexical semantic similarity resources , from which they derive features for a binary classifier---we initialize our word vectors with 300-dimensional word2vec word embeddings | 0 |
tam , lane and schultz also show improvements in machine translation using bilingual topic models---tam et al also explore a bilingual topic model for translation and language model adaptation | 1 |
in table 1 , database was listed among the top five terms that were most characteristic of the acl proceedings in 1979-1984---in table 1 , database was listed among the top five terms that were most characteristic of the acl proceedings | 1 |
lakoff and johnson , 1980 ) according to lakoff and johnson , a mapping of a concept of argument to that of war is employed here---lakoff and johnson , 1980 ) a mapping of a concept of argument to that of war is employed here | 1 |
the scores have been calculated using the reference implementation of the conll scorer---to verify sentence generation quantitatively , we evaluated the sentences automatically using bleu score | 0 |
following lample et al , the character-based representation is constructed with a bi-lstm---as mentioned above , the baseline model is a char-lstm-lstm-crf model | 1 |
erk et al also model selectional preferences using vector spaces---erk et al propose the exemplar-based model of selectional preferences , in turn based on erk | 1 |
the parameter weights are optimized with minimum error rate training---we demonstrate that the substitutability of connectives has significant effects on both distributional similarity | 0 |
semantic role labeling ( srl ) is the task of identifying the predicate-argument structure of a sentence---semantic role labeling ( srl ) is the process of assigning semantic roles to strings of words in a sentence according to their relationship to the semantic predicates expressed in the sentence | 1 |
in recent years , statistical approaches on atr ( automatic term recognition ) have achieved good results---in this paper , it can be regarded as semantic modelling of text sequences and handle the input sequences of varying length into a fixed-length vector | 0 |
relation extraction is the task of tagging semantic relations between pairs of entities from free text---relation extraction ( re ) is the task of recognizing relationships between entities mentioned in text | 1 |
socher et al model the two sentences with recursive neural networks , and then feed similarity scores between words and phrases to a cnn with dynamic pooling to capture sentence interactions---socher et al and socher et al present a framework based on recursive neural networks that learns vector space representations for multi-word phrases and sentences | 1 |
we exploit the svmlight-tk toolkit for kernel computation---we used the svm light package with a linear kernel | 1 |
vickrey et al built classifiers inspired by those used in wsd to fill in any blanks in a partially completed translation---for example , vickrey et al built classifiers inspired by those used in word sense disambiguation to fill in blanks in a partially-completed translation | 1 |
a pun is a word used in a context to evoke two or more distinct senses for humorous effect---a pun is a form of wordplay , which is often profiled by exploiting polysemy of a word or by replacing a phonetically similar sounding word for an intended humorous effect | 1 |
collins- thompson and callan adopted a similar approach and used a smoothed unigram model to predict the grade levels of short passages and web documents---collins- thompson and callan used a smoothed unigram language model to predict the grade reading levels of web page documents and short passages | 1 |
so , we input all the tweets into the automatic identification of dialectal arabic tool to perform token level language identification for the egy and msa tokens in context---for msa-egy , we used the automatic identification of dialectal arabic tool to perform token level language identification for the egy and msa tokens in context | 1 |
we use the opennlp pos tagger 4 to obtain pos tags and employ the maltparser for dependency parsing---the knowitall system also uses hyponym patterns to extract class instances from the web and evaluates them further by computing mutual information scores based on web queries | 0 |
for probabilities , we trained 5-gram language models using srilm---for the language model , we used srilm with modified kneser-ney smoothing | 1 |
identifying metaphorical word usage is important for reasoning about the implications of text---metaphorical and literal senses of a word will facilitate correct textual | 1 |
in this paper , we suggest a method for incorporating domain knowledge in semi-supervised learning algorithms---we are the first to suggest a general semi-supervised protocol that is driven by soft constraints | 1 |
the trigram language model is implemented in the srilm toolkit---choudhury et al , 2007 , used hidden markov model to simulate sms messages generation , considering the non-standard tokens in input sentence as emission state in hmm and labeling results are possible candidates | 0 |
we also use a 4-gram language model trained using srilm with kneser-ney smoothing---we use srilm for n-gram language model training and hmm decoding | 1 |
an adaptive approach , proposed by zhao and vogel , aims at mining parallel sentences from a bilingual comparable news collection collected from the web---zhao et al proposed a robust , adaptive approach for mining parallel sentences from a bilingual comparable news collection | 1 |
the phrase table was built using the scripts from the moses package---it is a standard phrasebased smt system built using the moses toolkit | 1 |
this paper introduces a new method for automatic style transfer---this paper introduces a novel approach to transferring style of a sentence | 1 |
overall , we achieve the new state-of-the-art on the msr-vtt dataset---that further improves results , achieving the new state-of-the-art on msr-vtt | 1 |
it has been shown that word embeddings are able to capture to certain semantic and syntactic aspects of words---linear combinations of word embedding vectors have been shown to correspond well to the semantic composition of the individual words | 1 |
sarcasm is a pervasive phenomenon in social media , permitting the concise communication of meaning , affect and attitude---sarcasm is a sophisticated speech act which commonly manifests on social communities such as twitter and reddit | 1 |
we use srilm to train a 5-gram language model on the target side of our training corpus with modified kneser-ney discounting---we used the sri language modeling toolkit to train a fivegram model with modified kneser-ney smoothing | 1 |
experimental results show that the pivot approach is effective , which extracts over 1,000,000 pairs of paraphrase patterns from 2m bilingual sentence pairs---experimental results show that the pivot approach evidently outperforms dirt , a well known method that extracts paraphrase patterns from monolingual corpora | 1 |
we extract all word pairs which occur as 1-to-1 alignments , and later refer to them as the list of word pairs---we extract all word pairs which occur as 1-to-1 alignments , and later refer to them as the word-aligned list | 1 |
also , neural network translation models show a success in smt---recently , convolutional neural networks are reported to perform well on a range of nlp tasks | 1 |
the berkeley framenet is an ongoing project for building a large lexical resource for english with expert annotations based on frame semantics---building on this frame-semantic model , the berkeley framenet project has been developing a frame-semantic lexicon for the core vocabulary of english since 1997 | 1 |
sense induction is typically treated as an unsupervised clustering problem---sense induction is thus typically viewed as an unsupervised clustering problem | 1 |
word sense disambiguation ( wsd ) is the task of identifying the correct meaning of a word in context---word sense disambiguation ( wsd ) is a key enabling-technology | 0 |
therefore , we can try to find the transformation that minimizes the earth mover ’ s distance---this paper presented a novel framework called error case frames for correcting preposition errors | 0 |
kohama et al improved the work of shibata and kurohashi by utilizing crowdsourced data for shared argument learning---we used the cmu tokenizer , 3 which is a sub-module of the cmu twitter pos tagger | 0 |
the core of the algorithm is a beam-search based decoder operating on the packed forest in a bottom-up manner---the core of the algorithm is a dynamic program for phrase-based translation which is efficient , but which allows some ill-formed translations | 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 the medium where people post real time messages to discuss on the different topics , and express their sentiments | 1 |
in this paper , we present a learning approach to coreference resolution of noun phrases in unrestricted text---in this paper , we focus on the task of determining coreference relations | 1 |
we have encoded lexical semantic spaces of different languages by means of the same pivot language in order to make the languages comparable---by pivoting on the reference language , we represent semantic associations among words in different languages by means of the synonymy relations of their translations | 1 |
we aim to extract frame-semantic structures from text---in their method , dep-dts are automatically transformed from ( auto-parsed ) | 0 |
we used the srilm software 4 to build langauge models as well as to calculate cross-entropy based features---we used srilm to build a 4-gram language model with kneser-ney discounting | 1 |
in this work , we also employ the bilstm architecture---in this paper , we introduce the kblstm network architecture | 1 |
while the performance of our estimator seems favorable , we also see that the widely used classical good-turing estimator consistently underestimates the vocabulary size---estimators ( both parametric and nonparametric ) on large standard corpora ; apart from showing the favorable performance of our estimator , we also see that the classical good-turing estimator consistently underestimates the vocabulary size | 1 |
in this work , we employ the toolkit word2vec to pre-train the word embedding for the source and target languages---this baseline uses pre-trained word embeddings using word2vec cbow and fasttext | 1 |
one key reason is that the objective functions of topic models do not correlate well with human judgements---however , perplexity on the heldout test set does not reflect the semantic coherence of topics and may be contrary to human judgments | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.