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What is the Essence of a Claim? Cross-Domain Claim Identification

Daxenberger, Johannes and Eger, Steffen and Habernal, Ivan and Stab, Christian and Gurevych, Iryna (2017):
What is the Essence of a Claim? Cross-Domain Claim Identification.
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, Denmark, [Online-Edition: http://aclweb.org/anthology/D17-1218],
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Daxenberger, Johannes and Eger, Steffen and Habernal, Ivan and Stab, Christian and Gurevych, Iryna
Title: What is the Essence of a Claim? Cross-Domain Claim Identification
Language: English
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.

Title of Book: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Uncontrolled Keywords: UKP_a_ArMin, UKP_s_DKPro_TC, UKP_p_ArguAna, UKP_p_ArgumenText
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Event Location: Copenhagen, Denmark
Date Deposited: 25 Apr 2017 18:55
Official URL: http://aclweb.org/anthology/D17-1218
Identification Number: TUD-CS-2017-0099
Projects: ArgumenText
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