2.50
Hdl Handle:
http://hdl.handle.net/2436/27900
Title:
Discovery of event entailment knowledge from text corpora
Authors:
Pekar, Viktor
Abstract:
Event entailment is knowledge that may prove useful for a variety of applications dealing with inferencing over events described in natural language texts. In this paper, we propose a method for automatic discovery of pairs of verbs related by entailment, such as X buy Y X own Y and appoint X as Y X become Y. In contrast to previous approaches that make use of lexico-syntactic patterns and distributional evidence, the underlying assumption of our method is that the implication of one event by another manifests itself in the regular co-occurrence of the two corresponding verbs within locally coherent text. Based on the analogy with the problem of learning selectional preferences Resnik’s [Resnik, P., 1993. Selection and information: a class-based approach to lexical relationships, Ph.D. Thesis, University of Pennsylvania] association strength measure is used to score the extracted verb pairs for asymmetric association in order to discover the direction of entailment in each pair. In our experimental evaluation, we examine the effect that various local discourse indicators produce on the accuracy of this model of entailment. After that we carry out a direct evaluation of the verb pairs against human subjects’ judgements and extrinsically evaluate the pairs on the task of noun phrase coreference resolution.
Citation:
Computer Speech & Language, 22 (1): 1-16
Publisher:
Elsevier
Journal:
Computer Speech & Language
Issue Date:
2008
URI:
http://hdl.handle.net/2436/27900
DOI:
10.1016/j.csl.2007.05.001
Additional Links:
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WCW-4NS2GG9-1&_user=1644469&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000054077&_version=1&_urlVersion=0&_userid=1644469&md5=1476e51eca3b92da92fe97d9673ae682
Type:
Article
Language:
en
ISSN:
08852308; 10958363
Appears in Collections:
Computational Linguistics Group; Computational Linguistics Group

Full metadata record

DC FieldValue Language
dc.contributor.authorPekar, Viktor-
dc.date.accessioned2008-05-23T16:39:51Z-
dc.date.available2008-05-23T16:39:51Z-
dc.date.issued2008-
dc.identifier.citationComputer Speech & Language, 22 (1): 1-16en
dc.identifier.issn08852308-
dc.identifier.issn10958363-
dc.identifier.doi10.1016/j.csl.2007.05.001-
dc.identifier.urihttp://hdl.handle.net/2436/27900-
dc.description.abstractEvent entailment is knowledge that may prove useful for a variety of applications dealing with inferencing over events described in natural language texts. In this paper, we propose a method for automatic discovery of pairs of verbs related by entailment, such as X buy Y X own Y and appoint X as Y X become Y. In contrast to previous approaches that make use of lexico-syntactic patterns and distributional evidence, the underlying assumption of our method is that the implication of one event by another manifests itself in the regular co-occurrence of the two corresponding verbs within locally coherent text. Based on the analogy with the problem of learning selectional preferences Resnik’s [Resnik, P., 1993. Selection and information: a class-based approach to lexical relationships, Ph.D. Thesis, University of Pennsylvania] association strength measure is used to score the extracted verb pairs for asymmetric association in order to discover the direction of entailment in each pair. In our experimental evaluation, we examine the effect that various local discourse indicators produce on the accuracy of this model of entailment. After that we carry out a direct evaluation of the verb pairs against human subjects’ judgements and extrinsically evaluate the pairs on the task of noun phrase coreference resolution.en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WCW-4NS2GG9-1&_user=1644469&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000054077&_version=1&_urlVersion=0&_userid=1644469&md5=1476e51eca3b92da92fe97d9673ae682en
dc.subjectLexical semanticsen
dc.subjectLexical entailmenten
dc.subjectLocal discourseen
dc.subjectCoreference resolutionen
dc.titleDiscovery of event entailment knowledge from text corporaen
dc.typeArticleen
dc.identifier.journalComputer Speech & Languageen
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