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

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2055–2066 Copenhagen, Denmark, September 7–11, 2017. c⃝2017 Association for Computational Linguistics What is the Essence of a Claim? Cross-Domain Claim Identification Johannes Daxenberger†, Steffen Eger†‡, Ivan Habernal†, Christian Stab†, Iryna Gurevych†‡ †Ubiquitous Knowledge Processing Lab (UKP-TUDA) Department of Computer Science, Technische Universit¨at Darmstadt ‡Ubiquitous Knowledge Processing Lab (UKP-DIPF) German Institute for Educational Research and Educational Information http://www.ukp.tu-darmstadt.de Abstract Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative compo- nents, e.g. claims, as the central compo- nent of an argument. We perform a quali- tative analysis across six different datasets and show that these appear to conceptu- alize claims quite differently. To learn about the consequences of such differ- ent conceptualizations of claim for prac- tical 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 conceptualization 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. 1 Introduction The key component of an argument is the claim. This simple observation has not changed much since the early works on argumentation by Aristo- tle more than two thousand years ago, although ar- gumentation scholars provide us with a plethora of often clashing theories and models (van Eemeren et al., 2014). Despite the lack of a precise defi- nition in the contemporary argumentation theory, Toulmin’s influential work on argumentation in the 1950’s introduced a claim as an ‘assertion that deserves our attention’ (Toulmin, 2003, p. 11); re- cent works describe a claim as ‘a statement that is in dispute and that we are trying to support with reasons’ (Govier, 2010). Argument mining, a computational counterpart of manual argumentation analysis, is a recent growing sub-field of NLP (Peldszus and Stede, 2013a). ‘Mining’ arguments usually involves sev- eral steps like separating argumentative from non- argumentative text units, parsing argument struc- tures, and recognizing argument components such as claims—the main focus of this article. Claim identification itself is an important prerequisite for applications such as fake checking (Vlachos and Riedel, 2014), politics and legal affairs (Surdeanu et al., 2010), and science (Park and Blake, 2012). Although claims can be identified with a promising level of accuracy in typical argumenta- tive discourse such as persuasive essays (Stab and Gurevych, 2014; Eger et al., 2017), less homo- geneous resources, for instance online discourse, pose challenges to current systems (Habernal and Gurevych, 2017). Furthermore, existing argument mining approaches are often limited to a single, specific domain like legal documents (Mochales- Palau and Moens, 2009), microtexts (Peldszus and Stede, 2015), Wikipedia articles (Levy et al., 2014; Rinott et al., 2015) or student essays (Stab and Gurevych, 2017). The problem of generalizing systems or features and their robustness across heterogeneous datasets thus remains fairly unex- plored. This situation motivated us to …

Content truncated to 500 words