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dc.contributor.authorMartinez, D
dc.contributor.authorde Lacalle, OL
dc.contributor.authorAgirre, E
dc.date.available2014-05-21T22:51:33Z
dc.date.issued2008-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000259611900002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationMartinez, D., de Lacalle, O. L. & Agirre, E. (2008). On the use of automatically acquired examples for all-nouns Word Sense Disambiguation. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 33 (1), pp.79-107. https://doi.org/10.1613/jair.2395.
dc.identifier.issn1076-9757
dc.identifier.urihttp://hdl.handle.net/11343/29299
dc.description.abstract<jats:p>This article focuses on Word Sense Disambiguation (WSD), which is a Natural Language Processing task that is thought to be important for many Language Technology applications, such as Information Retrieval, Information Extraction, or Machine Translation. One of the main issues preventing the deployment of WSD technology is the lack of training examples for Machine Learning systems, also known as the Knowledge Acquisition Bottleneck. A method which has been shown to work for small samples of words is the automatic acquisition of examples. We have previously shown that one of the most promising example acquisition methods scales up and produces a freely available database of 150 million examples from Web snippets for all polysemous nouns in WordNet. This paper focuses on the issues that arise when using those examples, all alone or in addition to manually tagged examples, to train a supervised WSD system for all nouns. The extensive evaluation on both lexical-sample and all-words Senseval benchmarks shows that we are able to improve over commonly used baselines and to achieve top-rank performance. The good use of the prior distributions from the senses proved to be a crucial factor.</jats:p>
dc.languageEnglish
dc.publisherAI ACCESS FOUNDATION
dc.subjectArtificial Intelligence and Image Processing
dc.titleOn the use of automatically acquired examples for all-nouns Word Sense Disambiguation
dc.typeJournal Article
dc.identifier.doi10.1613/jair.2395
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentComputer Science and Software Engineering
melbourne.source.titleJOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
melbourne.source.volume33
melbourne.source.issue1
melbourne.source.pages79-107
dc.description.pagestart79
melbourne.publicationid107258
melbourne.elementsid304044
melbourne.contributor.authorMartinez, David
dc.identifier.eissn1943-5037
melbourne.accessrightsThis item is currently not available from this repository


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