Habernal, Ivan ; Gurevych, Iryna (2015)
Exploiting Debate Portals for Semi-supervised Argumentation Mining in User-Generated Web Discourse.
Lisbon, Portugal
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Analyzing arguments in user-generated Web discourse has recently gained attention in argumentation mining, an evolving field of NLP. Current approaches, which employ fully-supervised machine learning, are usually domain dependent and suffer from the lack of large and diverse annotated corpora. However, annotating arguments in discourse is costly, error-prone, and highly context-dependent. We asked whether leveraging unlabeled data in a semi-supervised manner can boost the performance of argument component identification and to which extent is the approach independent of domain and register. We propose novel features that exploit clustering of unlabeled data from debate portals based on a word embeddings representation. Using these features, we significantly outperform several baselines in the cross-validation, cross-domain, and cross-register evaluation scenarios.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2015 |
| Autor(en): | Habernal, Ivan ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Exploiting Debate Portals for Semi-supervised Argumentation Mining in User-Generated Web Discourse |
| Sprache: | Englisch |
| Publikationsjahr: | September 2015 |
| Verlag: | Association for Computational Linguistics |
| Buchtitel: | Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP) |
| Veranstaltungsort: | Lisbon, Portugal |
| URL / URN: | http://www.aclweb.org/anthology/D15-1255 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Analyzing arguments in user-generated Web discourse has recently gained attention in argumentation mining, an evolving field of NLP. Current approaches, which employ fully-supervised machine learning, are usually domain dependent and suffer from the lack of large and diverse annotated corpora. However, annotating arguments in discourse is costly, error-prone, and highly context-dependent. We asked whether leveraging unlabeled data in a semi-supervised manner can boost the performance of argument component identification and to which extent is the approach independent of domain and register. We propose novel features that exploit clustering of unlabeled data from debate portals based on a word embeddings representation. Using these features, we significantly outperform several baselines in the cross-validation, cross-domain, and cross-register evaluation scenarios. |
| Freie Schlagworte: | UKP_a_ArMin;UKP_reviewed;argumentation mining |
| ID-Nummer: | TUD-CS-2015-1178 |
| 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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