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Inducing a Lexicon of Abusive Words – a Feature-Based Approach

  • We address the detection of abusive words. The task is to identify such words among a set of negative polar expressions. We propose novel features employing information from both corpora and lexical resources. These features are calibrated on a small manually annotated base lexicon which we use to produce a large lexicon. We show that the word-level information we learn cannot be equally derived from a large dataset of annotated microposts. We demonstrate the effectiveness of our (domain-independent) lexicon in the crossdomain detection of abusive microposts.

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Metadaten
Author:Michael WiegandGND, Josef RuppenhoferGND, Anna Schmidt, Clayton Greenberg
URN:urn:nbn:de:bsz:mh39-84719
URL:https://aclanthology.info/papers/N18-1095/n18-1095
DOI:https://doi.org/10.18653/v1/N18-1095
ISBN:978-1-948087-27-8
Parent Title (English):Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 1-June 6, 2018, New Orleans, Louisiana, Volume 1 (Long Papers)
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, PA
Document Type:Conference Proceeding
Language:English
Year of first Publication:2018
Date of Publication (online):2019/02/06
Creating Corporation:Association for Computational Linguistics
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
GND Keyword:Beleidigung; Computerlinguistik; Natürliche Sprache; Text Mining
First Page:1046
Last Page:1056
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Leibniz-Classification:Sprache, Linguistik
Linguistics-Classification:Computerlinguistik
Open Access?:Ja
Licence (English):License LogoCreative Commons - Attribution 4.0 International