Incremental cue phrase learning and bootstrapping method for causality extraction using cue phrase and word pair probabilities

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DC FieldValueLanguage
dc.contributor.authorChang, DSko
dc.contributor.authorChoi, Key-Sunko
dc.date.accessioned2008-03-31T01:54:22Z-
dc.date.available2008-03-31T01:54:22Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2006-05-
dc.identifier.citationINFORMATION PROCESSING & MANAGEMENT, v.42, no.3, pp.662 - 678-
dc.identifier.issn0306-4573-
dc.identifier.urihttp://hdl.handle.net/10203/3574-
dc.descriptionReceived 5 October 2004; accepted 11 April 2005. Available online 15 June 2005.en
dc.description.abstractThis work aims to extract possible causal relations that exist between noun phrases. Some causal relations are manifested by lexical patterns like causal verbs and their sub-categorization. We use lexical patterns as a filter to find causality candidates and we transfer the causality extraction problem to the binary classification. To solve the problem, we introduce probabilities for word pair and concept pair that could be part of causal noun phrase pairs. We also use the cue phrase probability that could be a causality pattern. These probabilities are learned from the raw corpus in an unsupervised manner. With this probabilistic model, we increase both precision and recall. Our causality extraction shows an F-score of 77.37%, which is an improvement of 21.14 percentage points over the baseline model. The long distance causal relation is extracted with the binary tree-styled cue phrase. We propose an incremental cue phrase learning method based on the cue phrase confidence score that was measured after each causal classifier learning step. A better recall of 15.37 percentage points is acquired after the cue phrase learning. (c) 2005 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.language.isoen_USen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.titleIncremental cue phrase learning and bootstrapping method for causality extraction using cue phrase and word pair probabilities-
dc.typeArticle-
dc.identifier.wosid000233552400005-
dc.identifier.scopusid2-s2.0-27644497673-
dc.type.rimsART-
dc.citation.volume42-
dc.citation.issue3-
dc.citation.beginningpage662-
dc.citation.endingpage678-
dc.citation.publicationnameINFORMATION PROCESSING & MANAGEMENT-
dc.identifier.doi10.1016/j.ipm.2005.04.004-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorChoi, Key-Sun-
dc.contributor.nonIdAuthorChang, DS-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorcausality-
dc.subject.keywordAuthorpattern learning-
dc.subject.keywordAuthorword pair probability-
dc.subject.keywordAuthorcue phrase probability-
dc.subject.keywordAuthorunsupervised learning-
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