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Negation modeling for German polarity classification

  • We present an approach for modeling German negation in open-domain fine grained sentiment analysis. Unlike most previous work in sentiment analysis, we assume that negation can be conveyed by many lexical units (and not only common negation words) and that different negation words have different scopes. Our approach is examined on a new dataset comprising sentences with mentions of polar expressions and various negation words. We identify different types of negation words that have the same scopes. We show that already negation modeling based on these types largely outperforms traditional negation models which assume the same scope for all negation words and which employ a window-based scope detection rather than a scope detection based on syntactic information.

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Metadaten
Author:Michael Wiegand, Maximilian Wolf, Josef RuppenhoferGND
URN:urn:nbn:de:bsz:mh39-69091
DOI:https://doi.org/10.1007/978-3-319-73706-5
ISBN:978-3-319-73705-8
Parent Title (English):Language technologies for the challenges of the digital age. 27th International Conference, GSCL 2017 Berlin, Germany, September 13 – 14, 2017. Proceedings
Series (Serial Number):Lecture Notes in Artificial Intelligence (10713)
Publisher:Springer
Place of publication:Cham, Switzerland
Document Type:Part of a Book
Language:English
Year of first Publication:2018
Date of Publication (online):2018/01/09
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
GND Keyword:Automatische Sprachanalyse; Deutsch; Negation; Semantische Analyse
First Page:95
Last Page:111
Dewey Decimal Classification:400 Sprache / 430 Deutsch
BDSL-Classification:Grammatik
Leibniz-Classification:Sprache, Linguistik
Linguistics-Classification:Grammatikforschung
Open Access?:Ja
Licence (English):License LogoCreative Commons - Attribution 4.0 International