Daxenberger, Johannes ; Eger, Steffen ; Habernal, Ivan ; Stab, Christian ; Gurevych, Iryna (2017)
What is the Essence of a Claim? Cross-Domain Claim Identification.
Copenhagen, Denmark
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 ; Eger, Steffen ; Habernal, Ivan ; Stab, Christian ; Gurevych, Iryna |
| Type of entry: | Bibliographie |
| Title: | What is the Essence of a Claim? Cross-Domain Claim Identification |
| Language: | English |
| Date: | September 2017 |
| Book Title: | Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP) |
| Event Location: | Copenhagen, Denmark |
| URL / URN: | http://aclweb.org/anthology/D17-1218 |
| 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. |
| Uncontrolled Keywords: | UKP_a_ArMin, UKP_s_DKPro_TC, UKP_p_ArguAna, UKP_p_ArgumenText |
| Identification Number: | TUD-CS-2017-0099 |
| 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 |
| Date Deposited: | 25 Apr 2017 18:55 |
| Last Modified: | 24 Jan 2020 12:03 |
| PPN: | |
| Projects: | ArgumenText |
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