A machine learning approach to personal pronoun resolution in Turkish

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dc.authorscopusid23096618000
dc.authorscopusid24481295000
dc.contributor.authorYildirim, S.
dc.contributor.authorKiliçaslan, Y.
dc.date.accessioned2024-07-18T20:18:00Z
dc.date.available2024-07-18T20:18:00Z
dc.date.issued2007
dc.descriptionFlorida Artificial Intelligence Research Society;Association for the Advancement of Artificial Intelligenceen_US
dc.description20th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007 -- 7 May 2007 through 9 May 2007 -- Key West, FL -- 70876en_US
dc.description.abstractIn this paper, we present a machine learning based approach for estimating antecedents of anaphorically used personal pronouns in Turkish sentences using a decision tree classification technique coupled with the ensemble learning method. The technique learns from an annotated corpus, which has been compiled mostly from various popular child stories. Copyright © 2007, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.en_US
dc.identifier.endpage270en_US
dc.identifier.isbn1577353196
dc.identifier.isbn9781577353195
dc.identifier.scopus2-s2.0-37349082785en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage269en_US
dc.identifier.urihttps://hdl.handle.net/11411/6837
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofProceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial İntelligenceen_US
dc.subjectDecision Treesen_US
dc.subjectEstimationen_US
dc.subjectNatural Language Processing Systemsen_US
dc.subjectAnnotated Corpusen_US
dc.subjectAntecedentsen_US
dc.subjectPersonal Pronoun Resolutionen_US
dc.subjectLearning Systemsen_US
dc.titleA machine learning approach to personal pronoun resolution in Turkishen_US
dc.typeConference Objecten_US

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