Issue framing in online discussion fora
Autor(en): | Hartmann, M. Jansen, T. Augenstein, I. Søgaard, A. |
Stichwörter: | Computational linguistics, Online discussions; Social media; Target domain; Training data, Social networking (online) | Erscheinungsdatum: | 2019 | Herausgeber: | Association for Computational Linguistics (ACL) | Enthalten in: | NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference | Band: | 1 | Startseite: | 1401 | Seitenende: | 1407 | Zusammenfassung: | In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in newswire and social media can be transferred to the domain of discussion fora, using a combination of multi-task and adversarial training, assuming only unlabeled training data in the target domain. © 2019 Association for Computational Linguistics |
Beschreibung: | Conference of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 ; Conference Date: 2 June 2019 Through 7 June 2019; Conference Code:159851 |
ISBN: | 9781950737130 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085584753&partnerID=40&md5=1478b5fab899d1b774371989b6675765 |
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geprüft am 08.06.2024