2.50
Hdl Handle:
http://hdl.handle.net/2436/27896
Title:
NP animacy identification for anaphora resolution
Authors:
Orasan, Constantin; Evans, Richard
Abstract:
In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.
Citation:
Journal of Artificial Intelligence Research, 29 (2007): 79-103
Publisher:
American Association for Artificial Intelligence
Journal:
Journal of Artificial Intelligence Research
Issue Date:
2007
URI:
http://hdl.handle.net/2436/27896
Additional Links:
http://www.jair.org/; http://www.jair.org/media/2179/live-2179-3273-jair.pdf
Type:
Article
Language:
en
ISSN:
11076 - 9757
Appears in Collections:
Computational Linguistics Group; Computational Linguistics Group

Full metadata record

DC FieldValue Language
dc.contributor.authorOrasan, Constantin-
dc.contributor.authorEvans, Richard-
dc.date.accessioned2008-05-23T15:45:46Z-
dc.date.available2008-05-23T15:45:46Z-
dc.date.issued2007-
dc.identifier.citationJournal of Artificial Intelligence Research, 29 (2007): 79-103en
dc.identifier.issn11076 - 9757-
dc.identifier.urihttp://hdl.handle.net/2436/27896-
dc.description.abstractIn anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.en
dc.language.isoenen
dc.publisherAmerican Association for Artificial Intelligenceen
dc.relation.urlhttp://www.jair.org/en
dc.relation.urlhttp://www.jair.org/media/2179/live-2179-3273-jair.pdfen
dc.subjectAnaphora resolutionen
dc.subjectAnimacy identificationen
dc.subjectArtificial Intelligence-
dc.titleNP animacy identification for anaphora resolutionen
dc.typeArticleen
dc.identifier.journalJournal of Artificial Intelligence Researchen
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