text stringlengths 82 736 | label int64 0 1 |
|---|---|
semantic parsing is the task of mapping natural language utterances to machine interpretable meaning representations---semantic parsing is the task of converting natural language utterances into formal representations of their meaning | 1 |
more recently , marquardt et al propose a multi-label classification approach to predict both the gender and age of authors from texts adopting some sentiment and emotion features---more recently , marquardt et al propose a multi-label classification approach to predict both the gender and age of authors from texts | 1 |
davidov et al propose utilizing twitter hashtag and smileys to learn enhanced sentiment types---davidov et al utilize hashtags and smileys to build a largescale annotated tweet dataset automatically | 1 |
the trigram language model is implemented in the srilm toolkit---the standard classifiers are implemented with scikit-learn | 0 |
the idea of inducing selectional preferences from corpora was introduced by resnik---one of the first approaches to the automatic induction of selectional preferences from corpora was the one by resnik | 1 |
we extend the pattern matching approach of cite-p-10-1-0 with machine learning techniques , and use dependency structures instead of constituency trees---we developed a similar approach using dependency structures rather than phrase structure trees , which , moreover , extends bare pattern matching with machine learning techniques | 1 |
dave et al discuss the major structural divergences with respect to english and hindi---dave et al have studied the language divergence between english and hindi | 1 |
we present a bootstrapping algorithm that automatically learns phrases corresponding to positive sentiments and phrases corresponding to negative situations---we present a novel bootstrapping algorithm that automatically learns lists of positive sentiment phrases and negative situation | 1 |
it is possible to compute the moore-penrose pseudoinverse using the svd in the following way---the language models used were 7-gram srilm with kneser-ney smoothing and linear interpolation | 0 |
named entity recognition ( ner ) is the task of identifying and classifying phrases that denote certain types of named entities ( nes ) , such as persons , organizations and locations in news articles , and genes , proteins and chemicals in biomedical literature---named entity recognition ( ner ) is a fundamental information extraction task that automatically detects named entities in text and classifies them into predefined entity types such as person , organization , gpe ( geopolitical entities ) , event , location , time , date , etc | 1 |
in other prior work ( cite-p-21-3-0 , cite-p-21-1-1 ) , the authors focused on identifying another type of event pair semantic relation : event coreference---in other prior work ( cite-p-21-3-0 , cite-p-21-1-1 ) , the authors focused on identifying another type of event pair | 1 |
in section 2.2 we elaborate on findings from related om research which also worked with movie reviews as this is our target domain in the present paper---in section 2 . 2 we elaborate on findings from related om research which also worked with movie reviews | 1 |
the baseline of our approach is a statistical phrase-based system which is trained using moses---as a baseline system for our experiments we use the syntax-based component of the moses toolkit | 1 |
we use the subjectivity lexicon of , 2 which contains approximately 8000 words which may be used to express opinions---in particular , we use the lexicon constructed for wilson et al , which contains about 8000 words | 1 |
significance testing is done using sign test by bootstrap re-sampling with 100 samples---the statistical significance test is performed by the re-sampling approach | 1 |
as baseline we use the state-of-the-art attention-based system of rush et al which relies on a feed-forward network decoder---empirically we show that our model beats the state-of-the-art systems of rush et al on multiple data sets | 1 |
comparing to their work , our mbrar approaches assume few about the question types , and all qa systems contribute in the reranking model---work is that , our mbrar approaches assume little about qa systems and can be easily applied to qa systems | 1 |
this is accomplished without manual feature engineering---that does not require extensive manual feature engineering | 1 |
baroni and zamparelli and guevara look at corpus-harvested phrase vectors to learn composition functions that should derive such composite vectors automatically---guevara and baroni and zamparelli introduce a different approach to model semantic compositionality in distributional spaces by extracting context vectors from the corpus also for the composed vector v3 | 1 |
we apply our model to the english portion of the conll 2012 shared task data , which is derived from the ontonotes corpus---for subtask c , we implemented a two-step strategy to select out the similar questions and filter the unrelated comments | 0 |
specifically , we use subtrees containing two or three words extracted from dependency trees in the auto-parsed data---and then we extract subtrees from dependency parsing trees in the auto-parsed data | 1 |
the asymmetric alignments are symmetrized with the intersection and the grow-diag-final-and heuristics---the obtained scfg is further used in a phrase-based and hierarchical phrase-based system | 0 |
we adopted the case-insensitive bleu-4 as the evaluation metric---we used the moses toolkit to build mt systems using various alignments | 0 |
we used a 4-gram language model which was trained on the xinhua section of the english gigaword corpus using the srilm 4 toolkit with modified kneser-ney smoothing---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit from stolcke | 1 |
word sense disambiguation ( wsd ) is the task to identify the intended sense of a word in a computational manner based on the context in which it appears ( cite-p-13-3-4 )---word sense disambiguation ( wsd ) is the problem of assigning a sense to an ambiguous word , using its context | 1 |
we carry out our experiments on chinese-english translation tasks using a reimplementation of the hierarchical phrase-based system---we use our implementation of hierarchical phrase-based smt , with standard features , for the smt experiments | 1 |
the weight parameter 位 is tuned by a minimum error-rate training algorithm---the mod- els h m are weighted by the weights 位 m which are tuned using minimum error rate training | 1 |
proposed by chiang , the hierarchical phrase-based machine translation model has achieved results comparable , if not superior , to conventional phrase-based approaches---the hierarchical phrase-based translation model has been widely adopted in statistical machine translation tasks | 1 |
word sense induction ( wsi ) is the task of automatically discovering all senses of an ambiguous word in a corpus---for improving shift-reduce parsing , we propose a novel neural model to predict the constituent hierarchy | 0 |
the translation quality is evaluated by caseinsensitive bleu-4 metric---the translation results are evaluated with case insensitive 4-gram bleu | 1 |
we initialize the word embeddings for our deep learning architecture with the 100-dimensional glove vectors---we use 300d glove vectors trained on 840b tokens as the word embedding input to the lstm | 1 |
for parameter optimization , we have used an online large margin algorithm called mira---to learn the weights associated with the parameters used in our model , we have used a learning framework called mira | 1 |
for pos-tagging , we used the stanford postagger---for pos tagging , we used the stanford pos tagger | 1 |
the language is a form of modal propositional logic---thurmair , 2009 ) summarized several different architectures of hybrid systems using smt and rbmt systems | 0 |
we use case-sensitive bleu-4 to measure the quality of translation result---we substitute our language model and use mert to optimize the bleu score | 1 |
wikipedia is the largest collection of encyclopedic data ever written in the history of humanity---wikipedia is a large , multilingual , highly structured , multi-domain encyclopedia , providing an increasingly large wealth of knowledge | 1 |
theories of the coherence of discourse and discourse relations have proved useful for the semantic interpretation of discourse---the pioneering work on building an automatic semantic role labeler was proposed by gildea and jurafsky | 0 |
we trained a 3-gram language model on the spanish side using srilm---huang et al , 2012 ) used the multi-prototype models to learn the vector for different senses of a word | 0 |
we initialize the embedding weights by the pre-trained word embeddings with 200 dimensional vectors---in addition , we utilize the pre-trained word embeddings with 300 dimensions from for initialization | 1 |
in our work , we use latent dirichlet allocation to identify the sub-topics in the given body of texts---to get the the sub-fields of the community , we use latent dirichlet allocation to find topics and label them by hand | 1 |
relation extraction is the task of finding semantic relations between two entities from text---semantic orientation of the phrase is not a mere sum of the orientations of the component words | 0 |
we propose a measure that takes into account each word¡¯s contribution to fluency and meaning---we propose a measure that assigns high scores to words and phrases that are likely to be redundant | 1 |
while the algorithm produced good lexicons for the task of learning to interpret navigation instructions , it only works in batch settings and does not scale well to large datasets---for their application of learning to interpret navigation instructions , it only works in batch settings and does not scale well to large datasets | 1 |
we optimise the feature weights of the model with minimum error rate training against the bleu evaluation metric---we use minimum error rate training to tune the feature weights of hpb for maximum bleu score on the development set with serval groups of different start weights | 1 |
we consider the standard phrase-based approach to mt---we induce a topic-based vector representation of sentences by applying the latent dirichlet allocation method | 0 |
for the classification task , we use pre-trained glove embedding vectors as lexical features---for this score we use glove word embeddings and simple addition for composing multiword concept and relation names | 1 |
we use skipgram model to train the embeddings on review texts for k-means clustering---in addition , we can use pre-trained neural word embeddings on large scale corpus for neural network initialization | 1 |
thus , we train a 4-gram language model based on kneser-ney smoothing method using sri toolkit and interpolate it with the best rnnlms by different weights---for this language model , we built a trigram language model with kneser-ney smoothing using srilm from the same automatically segmented corpus | 1 |
sentiment analysis ( sa ) is the research field that is concerned with identifying opinions in text and classifying them as positive , negative or neutral---sentiment analysis ( sa ) is a hot-topic in the academic world , and also in the industry | 1 |
bethard et al identify opinion propositions and their holders by semantic parsing techniques---bethard et al identify opinion holders by using semantic parsing techniques with additional linguistic features | 1 |
tang et al was first to incorporate user and product information into a neural network model for personalized rating prediction of products---in rating prediction research , tang et al embedded user and product level information into a neural network model | 1 |
such interfaces have clear task performance and user preference advantages over speech only interfaces , in particular for spatial tasks such as those involving maps---recent empirical results demonstrate clear task performance and user preference advantages for multimodal interfaces over speech only interfaces , in particular for spatial tasks such as those involving maps | 1 |
lstm has been successfully applied to a number of tasks related to speech and language processing , such as voice activity detection , speech recognition , and spoken language understanding---as a text classification task , we incorporate features of both lexicalresource-based and vector space semantics , including wordnet and verbnet sense-level information and vectorial word representations | 0 |
in this paper , we introduce a new lightweight context-aware model based on the attention encoder-decoder model proposed by bahdanau et al---sequence-to-sequence learningin this work , we follow the encoder-decoder architecture proposed by bahdanau et al | 1 |
snyder and barzilay propose a discriminative model for unsupervised morphological segmentation by using morphological chains to model the word formation process---snyder and barzilay consider learning morphological segmentation with nonparametric bayesian model from multilingual data | 1 |
sentence compression is the task of shortening a sentence while preserving its important information and grammaticality---sentence compression is a text-to-text generation task in which an input sentence must be transformed into a shorter output sentence which accurately reflects the meaning in the input and also remains grammatically well-formed | 1 |
for example , mcallester and givan ( 1992 ) introduce a syntax for first order logic which they call montagovian syntax---givan ( 1992 ) introduce a syntax for first order logic which they call montagovian syntax | 1 |
for this , we utilize the publicly available glove 1 word embeddings , specifically ones trained on the common crawl dataset---meanwhile , we adopt glove pre-trained word embeddings 5 to initialize the representation of input tokens | 1 |
coreference resolution is a challenging task , that involves identification and clustering of noun phrases mentions that refer to the same real-world entity---coreference resolution is the process of finding discourse entities ( markables ) referring to the same real-world entity or concept | 1 |
we adapted the moses phrase-based decoder to translate word lattices---we used the moses toolkit with its default settings to build three phrase-based translation systems | 1 |
palmer and dang et al argue that the use of syntactic frames and verb classes can simplify the definition of different verb senses---dreyer and eisner propose an infinite diriclet mixture model for capturing paradigms | 0 |
the automatic prediction of aspectual classes is very challenging for verbs whose aspectual value varies across readings , which are the rule rather than the exception---on a type level , this method does not give satisfying results for verbs whose aspectual value varies across readings ( henceforth ‘ aspectually polysemous verbs ’ ) , which are far from exceptional ( see section 3 ) | 1 |
additionally , coreference resolution is a pervasive problem in nlp and many nlp applications could benefit from an effective coreference resolver that can be easily configured and customized---coreference resolution is a set partitioning problem in which each resulting partition refers to an entity | 1 |
relation extraction is the task of finding relations between entities in text , which is useful for several tasks such as information extraction , summarization , and question answering ( cite-p-14-3-7 )---relation extraction is the key component for building relation knowledge graphs , and it is of crucial significance to natural language processing applications such as structured search , sentiment analysis , question answering , and summarization | 1 |
the encoder is a bidirectional lstm with 500 hidden units equally divided among the two directions---the encoder is implemented with a bi-directional lstm , and the decoder a uni-directional one | 1 |
the algorithm uses the same set of operations as earley 's ( 1970 ) algorithm for context-free grammars , but modified for unification grammars---for unification grammars is an extension of earley ' s algorithm ( cite-p-8-1-1 ) for context-free grammars | 1 |
huang and lowe implemented a hybrid approach to automated negation detection---this motivated huang and lowe to build a system based on syntax information | 1 |
compared with character-based methods , our model explicitly leverages word and word sequence information---character-based and word-based ner methods , our model has the advantage of leveraging explicit word information over character sequence labeling | 1 |
the paper presents an application of structural correspondence learning ( scl ) to parse disambiguation---the paper presents an application of structural correspondence learning ( scl ) ( cite-p-14-1-4 ) | 1 |
in addition , incorporating eye gaze with word confusion networks further improves performance---we have shown that incorporating eye gaze information improves reference resolution performance | 1 |
to deal with this problem , we propose a twin-candidate model for anaphora resolution---we extend this line of work to study the extent to which discriminative learning methods can lead to better generative language models | 0 |
we trained the five classifiers using the svm implementation in scikit-learn---we used the scikit-learn implementation of svrs and the skll toolkit | 1 |
we use 300d glove vectors trained on 840b tokens as the word embedding input to the lstm---we used glove vectors trained on common crawl 840b 4 with 300 dimensions as fixed word embeddings | 1 |
collobert et al used word embeddings as input to a deep neural network for multi-task learning---collobert et al use a convolutional neural network over the sequence of word embeddings | 1 |
the system dictionary of the mix-wp identifier is comprised of the ckip lexicon and those unknown words found automatically from the udn 2001 corpus by a chinese word autoconfirmation system---as textual features , we use the pretrained google news word embeddings , obtained by training the skip-gram model with negative sampling | 0 |
this maximum matching problem can be solved using the hungarian algorithm---this is solved by using the kuhn-munkres algorithm | 1 |
a context-free grammar ( cfg ) is a tuple math-w-2-5-5-22 , where vn and vt are finite , disjoint sets of nonterminal and terminal symbols , respectively , and s e vn is the start symbol---a context-free grammar ( cfg ) is a tuple math-w-3-1-1-9 , where math-w-3-1-1-22 is a finite set of nonterminal symbols , math-w-3-1-1-31 is a finite set of terminal symbols disjoint from n , math-w-3-1-1-44 is the start symbol and math-w-3-1-1-52 is a finite set of rules | 1 |
we also describe the experiments on news recommendation using the device-dependent readability and present their results---usefulness of the results , we apply the device-dependent readability to news article recommendation | 1 |
while there is a large body of work on bilingual comparable corpora , most of it is focused on learning word translations---under these preference rules can be found in polynomial time , while some alternative formalizations of the free-of-false-implicatures constraint make the generation task np-hard | 0 |
we used srilm for training the 5-gram language model with interpolated modified kneser-ney discounting ,---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 0 |
we also obtain the embeddings of each word from word2vec---additionally , we standardized the pos tagging schemes across languages , using the iiit-h pos tagset , which has 23 tags | 0 |
the summarization technique of barzilay and lee captures topic transitions in the text span by a hidden markov model , referred to as a content model---the sentiment analysis in twitter task of semeval-2013 provides 9,864 labeled tweets from twitter to be used as a training dataset | 0 |
we analyze a set of linguistic features in both truthful and deceptive responses to interview questions---we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit | 0 |
in this paper , we introduce automatic ¡®drunk-texting prediction¡¯ as a computational task---in this paper , we introduce automatic drunk-texting prediction as the task of predicting a tweet | 1 |
the language model storage of target language uses the implementation in kenlm which is trained and queried as a 5-gram model---after standard preprocessing of the data , we train a 3-gram language model using kenlm | 1 |
our phrase-based mt system is trained by moses with standard parameters settings---for our baseline we use the moses software to train a phrase based machine translation model | 1 |
morphological analysis is the basis for many nlp applications , including syntax parsing , machine translation and automatic indexing---that is , since the morphological analysis is the first-step in most nlp applications , the sentences with incorrect word spacing must be corrected for their further processing | 1 |
loglinear weighs were estimated by minimum errorrate training on the tune partition---feature weights are tuned using minimum error rate training on the 455 provided references | 1 |
therefore , dependency parsing is a potential “ sweet spot ” that deserves investigation---however , dependency parsing , which is a popular choice for japanese , can incorporate only shallow syntactic information , i.e. , pos tags , compared with the richer syntactic phrasal categories in constituency parsing | 1 |
the source and target sentences are tagged respectively using the treetagger and amira toolkits---we presented language muse , an open-access , web-based tool that can help content-area teachers | 0 |
for the semantic language model , we used the srilm package and trained a tri-gram language model with the default goodturing smoothing---output string is guaranteed to conform to a given target grammar | 0 |
coreference resolution is a central problem in natural language processing with a broad range of applications such as summarization ( cite-p-16-3-24 ) , textual entailment ( cite-p-16-3-12 ) , information extraction ( cite-p-16-3-11 ) , and dialogue systems ( cite-p-16-3-25 )---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 |
analogical learning over strings has been investigated by several authors---analogical learning over strings is a holistic model that has been investigated by a few authors | 1 |
twitter consists of a massive number of posts on a wide range of subjects , making it very interesting to extract information and sentiments from them---the language model used was a 5-gram with modified kneserney smoothing , built with srilm toolkit | 0 |
wikipedia is a web based , freely available multilingual encyclopedia , constructed in a collaborative effort by thousands of contributors---we used a 5-gram language model trained on 126 million words of the xinhua section of the english gigaword corpus , estimated with srilm | 0 |
marton et al explored the contribution of different pos tag sets and several lexical and inflectional morphology features to dependency parsing of arabic---marton et al also explore which morphological features could be useful in dependency parsing of arabic | 1 |
we use the collapsed tree formalism of the stanford dependency parser---our baseline is a phrase-based mt system trained using the moses toolkit | 0 |
we used the stanford parser to generate dependency trees of sentences---task is to filter the abusive words from a given set of negative polar expressions | 0 |
it follows the distant supervision paradigm and performs knowledge-based label transfer from rich external knowledge sources to large corpora---and performs knowledge-based label transfer from rich external knowledge sources to large-scale corpora | 1 |
abstract meaning representation is a semantic formalism where the meaning of a sentence is encoded as a rooted , directed graph---abstract meaning representation is a sembanking language that captures whole sentence meanings in a rooted , directed , labeled , and acyclic graph structure | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.