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A machine learning approach to pronoun resolution in spoken dialogue

  • We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NP- and non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.

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Metadaten
Author:Michael StrubeGND, Mark-Christoph MüllerORCiDGND
URN:urn:nbn:de:bsz:mh39-111560
URL:https://aclanthology.org/P03-1022
DOI:https://doi.org/10.3115/1075096.1075118
Parent Title (English):Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics. July 7 - 12, 2003, Sapporo, Japan
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, Pennsylvania
Document Type:Conference Proceeding
Language:English
Year of first Publication:2003
Date of Publication (online):2022/07/26
Publishing Institution:Leibniz-Institut für Deutsche Sprache (IDS)
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
GND Keyword:Dialog; Entscheidungsbaum; Gesprochene Sprache; Korpus <Linguistik>; Maschinelles Lernen; Nominalphrase; Pronomen
First Page:168
Last Page:175
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Linguistics-Classification:Computerlinguistik
Licence (English):License LogoCreative Commons - Attribution-NonCommercial-ShareAlike 3.0 Unported