text stringlengths 82 736 | label int64 0 1 |
|---|---|
for language model , we use a trigram language model trained with the srilm toolkit on the english side of the training corpus---for language model , we used sri language modeling toolkit to train a 4-gram model with modified kneser-ney smoothing | 1 |
recent work addresses this problem by scoring a particular dimension of essay quality such as coherence , technical errors , relevance to prompt , and organization---ibm models and the hidden markov model for word alignment are the most influential statistical word alignment models | 0 |
in this paper we presented a model to effectively include semantics in lexical-syntactic features for textual entailment recognition---in this paper , we propose models for effectively using syntactic and semantic information | 1 |
berg-kirkpatrick et al proposed a joint model of sentence extraction and compression for multi-document summarization---berg-kirkpatrick et al formulated a unified task of sentence extraction and sentence compression as an ilp | 1 |
to implement svm algorithm , we have used the publicly available python based scikit-learn package---coreference resolution is a key problem in natural language understanding that still escapes reliable solutions | 0 |
hassan and menezes proposed an approach for normalizing social media text which used random walk framework on a contextual similarity bipartite graph constructed from n-grams sequences , which they interpolated with edit distance---wikipedia is a free , collaboratively edited encyclopedia | 0 |
we use mteval from the moses toolkit and tercom to evaluate our systems on the bleu and ter measures---we test the performance of our system on the test and eval sets using the bleu and translation edit rate measures | 1 |
for the task of event trigger prediction , we train a multi-class logistic regression classifier using liblinear---in order to perform efficient inference and learning , we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable , and employ these approximated distributions | 0 |
text categorization is the classificationof documents with respect to a set of predefined categories---text categorization is the classification of documents with respect to a set of predefined categories | 1 |
first , we extract the named entities in the text using stanford corenlp---however , wsd is a difficult task , and despite the fact that it has been the focus of much research over the years , state-of-the-art systems are still often not good enough for real-world applications | 0 |
syntactic analysis for syntactic features , we trained an arabic dependency parser using maltparser on the columbia arabic treebank version of the patb ,---we trained an arabic dependency parser using maltparser on the columbia arabic treebank version of the patb | 1 |
our approach , called ¡®iterated reranking¡¯ ( ir ) , starts with dependency trees generated by an unsupervised parser , and iteratively improves these trees using the richer probability models used in supervised parsing that are in turn trained on these trees---we obtain these dependency constructions by implementing a distantly supervised pattern extraction approach | 0 |
we follow a method commonly used in smt to extract bi-phrases and estimate their replacement probabilities---finally , we compute the translation probabilities according to the estimated co-occurrence counts , using the standard training method in phrase-based smt | 1 |
as monolingual baselines , we use the skip-gram and cbow methods of mikolov et al as implemented in the gensim package---we first obtain word representations using the popular skip-gram model with negative sampling introduced by mikolov et al and implemented in the gensim package | 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 fundamental problem aiming to give a machine the ability to understand the emotions and opinions expressed in a written text | 1 |
we used kneser-ney smoothing for training bigram language models---the language model is a large interpolated 5-gram lm with modified kneser-ney smoothing | 1 |
a 5-gram language model was built using srilm on the target side of the corresponding training corpus---the pre-processed monolingual sentences will be used by srilm or berkeleylm to train a n-gram language 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 mapping specific textual mentions of entities in a text document to an entry in a large catalog of entities , often called a knowledge base or kb , and is one of the major tasks in the knowledge-base population track at the text analysis conference ( tac ) ( cite-p-23-3-1 ) | 1 |
previous such work operates at the word level---the most widely used approach works at the word level | 1 |
the tjp system participated in semeval 2014 task 9 , part a : contextual polarity disambiguation---tjp system which was submitted to semeval 2014 task 9 , part a : contextual polarity disambiguation | 1 |
all features were log-linearly combined and their weights were optimized by performing minimum error rate training---feature weights were set with minimum error rate training on a tuning set using bleu as the objective function | 1 |
reisinger and mooney and huang et al use context clustering to induce multiple word senses for a target word type , where each sense is represented by a different context feature vector---in an attempt to capture the different senses or usage of a word , reisinger and mooney and huang et al proposed multi-prototype models for inducing multiple embeddings for each word | 1 |
in the lexical simplification subtask , existing methods differ in their decision to include a word sense disambiguation ( wsd ) step for substitute selection and in the ranking method used---wordnet is a manually created lexical database that organizes a large number of english words into sets of synonyms ( i.e . synsets ) and records conceptual relations ( e.g. , hypernym , part of ) among them | 0 |
to solve this problem , hochreiter and schmidhuber introduced the long short-term memory rnn---long short term memory units are proposed in hochreiter and schmidhuber to overcome this problem | 1 |
but this model suffers from the problem that the number of transition actions is not identical for different hypotheses in decoding , leading to the failure of performing optimal search---transitions for each hypothesis path is not identical to 2 ? n , which leads to the failure of performing optimal search during decoding | 1 |
phrase-based statistical machine translation models have achieved significant improvements in translation accuracy over the original ibm word-based model---we apply the rules to each sentence with its dependency tree structure acquired from the stanford parser | 0 |
relation classification is the task of assigning sentences with two marked entities to a predefined set of relations---relation classification is the task of finding semantic relations between pairs of nominals , which is useful for many nlp applications , such as information extraction ( cite-p-15-3-3 ) , question answering ( cite-p-15-3-6 ) | 1 |
in this paper , we propose a syllable-based method for tweet normalization to study the cognitive process of non-standard word creation in social media---in this paper , we propose a syllable-based method for tweet normalization to study the cognitive process of non-standard word creation | 1 |
we used svmlight together with the user defined kernel setting in our approach---our framework was built with the cleartk toolkit with its wrapper for svmlight | 1 |
coreference resolution is the problem of partitioning a sequence of noun phrases ( or mentions ) , as they occur in a natural language text , into a set of referential entities---coreference resolution is the task of determining whether two or more noun phrases refer to the same entity in a text | 1 |
transitionbased and graph-based models have attracted the most attention of dependency parsing in recent years---transition-based and graph-based have attracted the most attention in recent years | 1 |
mcclosky et al used a two phase parser-reranker system for self-training using readily available raw data---mcclosky et al showed that self-training improves parsing accuracy when the two-stage charniak and johnson reranking parser is used | 1 |
speriosu , sudan et al demonstrated that using label propagation with twitter follower graph improves the polarity classification---in they demonstrated that using label propagation with twitter follower graph improves the polarity classification | 1 |
turian et al learned a crf model using word embeddings as input features for ner and chunking tasks---rothe and sch眉tze , 2015 ) build a neural-network post-processing system called autoextend that takes word embeddings and learns embeddings for synsets and lexemes | 0 |
hochreiter and schmidhuber , 1997 ) proposed a long short-term memory network , which can be used for sequence processing tasks---hochreiter and schmidhuber developed long short-term memory to overcome the long term dependency problem | 1 |
after entity alignment , these labeled and unlabeled instances in both languages are fed into a bilingual active learning engine---the x-lingual method uses unlabeled parallel sentences to induce cross-lingual word clusters as augmenting features for delexicalized dependency parser | 0 |
the key linguistic insight behind our approach is selectional preference of connotative predicates---to verify sentence generation quantitatively , we evaluated the sentences automatically using bleu score | 0 |
to solve this problem , hochreiter and schmidhuber introduced the long short-term memory rnn---hochreiter and schmidhuber developed long short-term memory to overcome the long term dependency problem | 1 |
for the feature-based system we used logistic regression classifier from the scikit-learn library---second , our model achieves the best results to date on the kbp 2016 english and chinese event | 0 |
the task of relation extraction ( re ) consists of detecting and classifying the semantic relations present in text---the task of endto-end relation extraction consists of three subtasks : i ) identifying boundaries of entity mentions , ii ) identifying entity types of these mentions and iii ) identifying appropriate semantic relation for each pair of mentions | 1 |
in contrast , the rule extraction method of galley et al aims to incorporate more syntactic information by providing parse trees for the target language and extracting tree transducer rules that apply to the parses---the lms are build using the srilm language modelling toolkit with modified kneserney discounting and interpolation | 0 |
for the actioneffect embedding model , we use pre-trained glove word embeddings as input to the lstm---we train randomly initialized word embeddings of size 500 for the dialog model and use 300 dimentional glove embeddings for reranking classifiers | 1 |
experimental results on two datasets show that it outperforms prior approaches by modeling intermediate path nodes---experiments on two datasets show that it addresses representational limitations in prior approaches | 1 |
experimentation on chinese-to-english translation demonstrates that all proposed approaches are able to improve the translation accuracy---all the weights of those features are tuned by using minimal error rate training | 0 |
hawes , lin , and cite-p-16-7-11 use a conditional random fields ( crf ) model to predict the next speaker in supreme court oral argument transcripts---hawes , lin , and cite-p-16-7-11 use a conditional random fields ( crf ) model to predict the next speaker | 1 |
we then lowercase all data and use all sentences from the modern dutch part of the corpus to train an n-gram language model with the srilm toolkit---we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus | 1 |
the probabilistic language model is constructed on google web 1t 5-gram corpus by using the srilm toolkit---the target-side language models were estimated using the srilm toolkit | 1 |
for the cluster- based method , we use word2vec 2 which provides the word vectors trained on the google news corpus---we then used cluto to cluster attributes using these vectorial representations | 0 |
cardie and wagstaff proposed an unsupervised approach that casts the problem of coreference resolution as a clustering task that applies a set of incompatibility functions and weights in the distance metric---cardie and wagstaff re-cast the problem as a clustering task which applied a set of incompatibility functions and weights in the distance metric | 1 |
in the first phase , the post plus its responses are classified into four categories based on the intention , interrogation , sharing , discussion and chat---and it leads to faster translation speed and better translation quality due to the reduced search space | 0 |
we build all the classifiers using the l2-regularized linear logistic regression from the liblinear package---we used a logistic regression classifier provided by the liblinear software | 1 |
huang et al , 2012 ) used the multi-prototype models to learn the vector for different senses of a word---huang et al , 2012 , build a similar model using k-means clustering , but also incorporate global textual features into initial context vectors | 1 |
we preinitialize the word embeddings by running the word2vec tool on the english wikipedia dump---semantic parsing is the task of mapping natural language sentences to complete formal meaning representations | 0 |
gimpel et al and foster et al annotated english microblog posts with pos tags---to induce interlingual features , several resources have been used , including bilingual lexicon and parallel corpora | 0 |
coreference resolution is the task of identifying all mentions which refer to the same entity in a document---coreference resolution is a multi-faceted task : humans resolve references by exploiting contextual and grammatical clues , as well as semantic information and world knowledge , so capturing each of these will be necessary for an automatic system to fully solve the problem | 1 |
experimental results illustrate that our method outperforms several baseline systems---aspect extraction is a central problem in sentiment analysis | 0 |
machine comprehension of text is the overarching goal of a great deal of research in natural language processing---machine comprehension of text is the central goal in nlp | 1 |
we use a cws-oriented model modified from the skip-gram model to derive word embeddings---for other neural models , we employ skip-gram model to pre-train word embeddings with the embedding size of 100 | 1 |
in this paper , we show that the nystro ? m based low-rank embedding of input examples can be used as the early layer of a deep feed-forward neural network---in this paper , we show that expressive kernels and deep neural networks can be combined in a common framework in order to ( i ) explicitly model structured information | 1 |
lstms were introduced by hochreiter and schmidhuber in order to mitigate the vanishing gradient problem---to solve the traditional recurrent neural networks , hochreiter and schmidhuber proposed the lstm architecture | 1 |
on several data conditions , we show that our method outperforms the baseline and results in up to 8.5 % improvement in the f 1 -score---this paper proposes a novel framework for a large-scale , accurate acquisition method for monolingual semantic knowledge , especially for semantic | 0 |
we adapted the moses phrase-based decoder to translate word lattices---inspired by the hypothesis of one sense per discourse , ji and grishman combined global evidence from related documents with local decisions for the event extraction | 0 |
zelenko et al proposed a tree kernel over shallow parse tree representations of sentences---zelenko et al used the kernel methods for extracting relations from text | 1 |
the nodes are concepts ( or synsets as they are called in the wordnet )---wordnet is a byproduct of such an analysis | 1 |
the attentionbased recurrent neural network version of this architecture has been a very popular approach to nmt---most nmt models are based on the sequence-tosequence approach , and the rnn-based architecture with attention is a popular version of such an approach | 1 |
word alignment is a critical first step for building statistical machine translation systems---word alignment is the process of identifying wordto-word links between parallel sentences | 1 |
we optimized each system separately using minimum error rate training---we use minimum error rate training to tune the decoder | 1 |
wikipedia is a constantly evolving source of detailed information that could facilitate intelligent machines — if they are able to leverage its power---wikipedia is the largest collection of encyclopedic data ever written in the history of humanity | 1 |
xu et al and yu and dredze exploited semantic knowledge to improve the semantic representation of word embeddings---yu and dredze proposed a model to learn word embeddings based on lexical relations of words from wordnet and ppdb | 1 |
in this paper , we show that using well calibrated probabilities to estimate sense priors is important---in this paper , we explore the estimation of sense priors by first calibrating the probabilities from naive | 1 |
we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing---for the language model , we used srilm with modified kneser-ney smoothing | 1 |
in particular , it has been proven that inversion transduction grammar , which captures structural coherence between parallel sentences , helps in word alignment---recently , inversion transduction grammars , namely itg , have been used to constrain the search space for word alignment | 1 |
coreference resolution is a field in which major progress has been made in the last decade---coreference resolution is the process of linking together multiple referring expressions of a given entity in the world | 1 |
one of the main stumbling blocks for spoken dialogue systems is the lack of reliability of automatic speech recognizers---one of the main stumbling blocks for spoken natural language understanding systems is the lack of reliability of automatic speech recognizers | 1 |
this means in practice that the language model was trained using the srilm toolkit---the target-side language models were estimated using the srilm toolkit | 1 |
this task is part of the news evaluation campaign conducted in 2009---sentence compression is the task of producing a summary at the sentence level | 0 |
we use the moses package to train a phrase-based machine translation model---we use the moses smt toolkit to test the augmented datasets | 1 |
we use scikit-learn to implement the classifiers and accuracy scores to measure the predictability---in the present paper , we directly acquire inter-topic preferences | 0 |
for the tree-based system , we applied a 4-gram language model with kneserney smoothing using srilm toolkit trained on the whole monolingual corpus---without using any explicit delimiting character , detection of unknown words could be accomplished mainly by using a word-segmentation algorithm with a morphological analysis | 0 |
for all classifiers , we used the scikit-learn implementation---we use the svm implementation from scikit-learn , which in turn is based on libsvm | 1 |
we perform minimum-error-rate training to tune the feature weights of the translation model to maximize the bleu score on development set---for language model , we train a 5-gram modified kneser-ney language model and use minimum error rate training to tune the smt | 1 |
in this paper , we have proposed a task-independent , general method to analyse annotation schemes---in this paper , we compare the two annotation schemes , analysing how well they fare | 1 |
sentence compression is the task of compressing long sentences into short and concise ones by deleting words---we measured the overall translation quality with the help of 4-gram bleu , which was computed on tokenized and lowercased data for both systems | 0 |
we present the nrc vad lexicon , which has human ratings of valence , arousal , and dominance for more than 20,000 english words---in this paper , we describe how we obtained human ratings of valence , arousal , and dominance for more than 20 , 000 commonly used english words | 1 |
the data collection methods used to compile the dataset used in offenseval is described in zampieri et al---the data collection methods used to compile the dataset provided in offenseval is described in zampieri et al | 1 |
we have created a supervised version of the noisy-channel model with some improvements over the k & m model---in addition to improving the original k & m noisy-channel model , we create unsupervised and semi-supervised models of the task | 1 |
for language modeling , we use the english gigaword corpus with 5-gram lm implemented with the kenlm toolkit---for language modeling , we use kenlm to train 6-gram character-level language models on opensubs f iltered and huawei m onot r | 1 |
we describe the semeval-2010 shared task on “ linking events and their participants in discourse ”---we described the semeval-2010 shared task on “ linking events and their participants in discourse ” | 1 |
for example , faruqui et al introduce knowledge in lexical resources into the models in word2vec---faruqui et al use synonym relations extracted from wordnet and other resources to construct an undirected graph | 1 |
we use 300 dimension word2vec word embeddings for the experiments---we use the 300-dimensional skip-gram word embeddings built on the google-news corpus | 1 |
blitzer et al proposed structural correspondence learning to identify the correspondences among features between different domains via the concept of pivot features---blitzer et al investigate domain adaptation for pos tagging using the method of structural correspondence learning | 1 |
in this paper , we argue for the integration of top down ( theory based ) information into nlp---in this paper , we present a formalization of grammatical role labeling | 1 |
word sense disambiguation ( wsd ) is the task of identifying the correct sense of an ambiguous word in a given context---we apply the rules to each sentence with its dependency tree structure acquired from the stanford parser | 0 |
reinforcement learning is a machine learning technique that defines how an agent learns to take optimal actions so as to maximise a cumulative reward---reinforcement learning is a machine learning technique that defines how an agent learns to take optimal sequences of actions so as to maximize a cumulative reward | 1 |
all these problems harm the generalization ability of search-based structured prediction and lead to poor performance---table 4 shows the bleu scores of the output descriptions | 0 |
some methods , ananiadou , rely purely on linguistic information , namely morpho-syntactic features of term candidates---we evaluate the translation quality using the case-insensitive bleu-4 metric | 0 |
related work bharati et al has described a constraint based hindi parser by applying the paninian framework---the target language model is built on the target side of the parallel data with kneser-ney smoothing using the irstlm tool | 0 |
experimental evaluation on the a tis dataset shows that our model attains significantly higher fluency and semantic correctness than any of the comparison systems---collobert et al first introduced an end-to-end neural-based approach with sequence-level training and uses a convolutional neural network to model the context window | 0 |
semantic parsing is the task of mapping natural language to a formal meaning representation---table 1 shows the translation performance by bleu | 0 |
we use bleu scores to measure translation accuracy---we report bleu scores to compare translation results | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.