Habernal, Ivan ; Gurevych, Iryna (2017)
Argumentation Mining in User-Generated Web Discourse.
In: Computational Linguistics, 43 (1)
doi: 10.1162/COLI_a_00276
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Habernal, Ivan ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Argumentation Mining in User-Generated Web Discourse |
| Sprache: | Englisch |
| Publikationsjahr: | April 2017 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Computational Linguistics |
| Jahrgang/Volume einer Zeitschrift: | 43 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.1162/COLI_a_00276 |
| URL / URN: | http://dx.doi.org/10.1162/COLI_a_00276 |
| Kurzbeschreibung (Abstract): | The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task. |
| Freie Schlagworte: | UKP_a_ArMin |
| ID-Nummer: | TUD-CS-2016-0013 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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