@inproceedings{daxenberger-etal-2017-essence,
title = "What is the Essence of a Claim? Cross-Domain Claim Identification",
author = "Daxenberger, Johannes and
Eger, Steffen and
Habernal, Ivan and
Stab, Christian and
Gurevych, Iryna",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1218",
doi = "10.18653/v1/D17-1218",
pages = "2055--2066",
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 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.",
}
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<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 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.</abstract>
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%0 Conference Proceedings
%T What is the Essence of a Claim? Cross-Domain Claim Identification
%A Daxenberger, Johannes
%A Eger, Steffen
%A Habernal, Ivan
%A Stab, Christian
%A Gurevych, Iryna
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F daxenberger-etal-2017-essence
%X 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 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.
%R 10.18653/v1/D17-1218
%U https://aclanthology.org/D17-1218
%U https://doi.org/10.18653/v1/D17-1218
%P 2055-2066
Markdown (Informal)
[What is the Essence of a Claim? Cross-Domain Claim Identification](https://aclanthology.org/D17-1218) (Daxenberger et al., EMNLP 2017)
ACL
- Johannes Daxenberger, Steffen Eger, Ivan Habernal, Christian Stab, and Iryna Gurevych. 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, pages 2055–2066, Copenhagen, Denmark. Association for Computational Linguistics.