Daxenberger, Johannes ; Eger, Steffen ; Habernal, Ivan ; Stab, Christian ; Gurevych, Iryna (2017)
What is the Essence of a Claim? Cross-Domain Claim Identification.
Copenhagen, Denmark
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent perception of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Daxenberger, Johannes ; Eger, Steffen ; Habernal, Ivan ; Stab, Christian ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | What is the Essence of a Claim? Cross-Domain Claim Identification |
| Sprache: | Englisch |
| Publikationsjahr: | September 2017 |
| Buchtitel: | Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP) |
| Veranstaltungsort: | Copenhagen, Denmark |
| URL / URN: | http://aclweb.org/anthology/D17-1218 |
| Kurzbeschreibung (Abstract): | Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent perception of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps. |
| Freie Schlagworte: | UKP_a_ArMin, UKP_s_DKPro_TC, UKP_p_ArguAna, UKP_p_ArgumenText |
| ID-Nummer: | TUD-CS-2017-0099 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 25 Apr 2017 18:55 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
| PPN: | |
| Projekte: | ArgumenText |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum