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Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction

  • In this paper, we compare three different generalization methods for in-domain and cross-domain opinion holder extraction being simple unsupervised word clustering, an induction method inspired by distant supervision and the usage of lexical resources. The generalization methods are incorporated into diverse classifiers. We show that generalization causes significant improvements and that the impact of improvement depends on the type of classifier and on how much training and test data differ from each other. We also address the less common case of opinion holders being realized in patient position and suggest approaches including a novel (linguistically-informed) extraction method how to detect those opinion holders without labeled training data as standard datasets contain too few instances of this type.

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Metadaten
Author:Michael WiegandGND, Dietrich Klakow
URN:urn:nbn:de:bsz:mh39-84378
URL:https://dl.acm.org/citation.cfm?id=2380857
ISBN:978-1-937284-19-0
Parent Title (English):Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, April 23-27 2012, Avignon France
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, PA
Document Type:Conference Proceeding
Language:English
Year of first Publication:2012
Date of Publication (online):2019/01/23
Publicationstate:Zweitveröffentlichung
Reviewstate:Peer-Review
Tag:Sentimentanalyse
GND Keyword:Computerlinguistik; Information Extraction; Maschinelles Lernen; Meinung; Natürliche Sprache
First Page:325
Last Page:335
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Linguistics-Classification:Computerlinguistik
Open Access?:Ja
Licence (German):Es gilt das UrhG