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Convolution Kernels for Opinion Holder Extraction

  • Opinion holder extraction is one of the important subtasks in sentiment analysis. The effective detection of an opinion holder depends on the consideration of various cues on various levels of representation, though they are hard to formulate explicitly as features. In this work, we propose to use convolution kernels for that task which identify meaningful fragments of sequences or trees by themselves. We not only investigate how different levels of information can be effectively combined in different kernels but also examine how the scope of these kernels should be chosen. In general relation extraction, the two candidate entities thought to be involved in a relation are commonly chosen to be the boundaries of sequences and trees. The definition of boundaries in opinion holder extraction, however, is less straightforward since there might be several expressions beside the candidate opinion holder to be eligible for being a boundary.

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Metadaten
Author:Michael WiegandGND, Dietrich Klakow
URN:urn:nbn:de:bsz:mh39-84345
URL:https://dl.acm.org/citation.cfm?id=1858120
ISBN:978-1-932432-65-7
Parent Title (English):Proceedings of HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, June 2–4, 2010, Los Angeles, California
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, PA
Document Type:Conference Proceeding
Language:English
Year of first Publication:2010
Date of Publication (online):2019/01/22
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
Tag:Sentimentanalyse
GND Keyword:Computerlinguistik; Information Extraction; Maschinelles Lernen; Meinung; Natürliche Sprache
First Page:795
Last Page:803
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Linguistics-Classification:Computerlinguistik
Open Access?:Ja
Licence (German):Es gilt das UrhG