Cross-Domain Mining of Argumentative Text through Distant Supervision

verfasst von

Khalid Al-Khatib, Henning Wachsmuth, Matthias Hagen, Jonas Köhler, Benno Stein

Abstract

Argumentation mining is considered as a key technology for future search engines and automated decision making. In such applications, argumentative text segments have to be mined from large and diverse document collections. However, most existing argumentation mining approaches tackle the classification of argumentativeness only for a few manually annotated documents from narrow domains and registers. This limits their practical applicability. We hence propose a distant supervision approach that acquires argumentative text segments automatically from online debate portals. Experiments across domains and registers show that training on such a corpus improves the effectiveness and robustness of mining argumentative text. We freely provide the underlying corpus for research.

Externe Organisation(en)

Bauhaus-Universität Weimar

Typ

Aufsatz in Konferenzband

Seiten

1395-1404

Anzahl der Seiten

10

Publikationsdatum

06.2016

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Angewandte Informatik, Linguistik und Sprache, Sprache und Linguistik

Elektronische Version(en)

https://doi.org/10.18653/v1/n16-1165 (Zugang: Offen)