Unsupervised corpus–wide claim detection

Ran Levy, Shai Gretz, Benjamin Sznajder, Shay Hummel, Ranit Aharonov, Noam Slonim


Abstract
Automatic claim detection is a fundamental argument mining task that aims to automatically mine claims regarding a topic of consideration. Previous works on mining argumentative content have assumed that a set of relevant documents is given in advance. Here, we present a first corpus– wide claim detection framework, that can be directly applied to massive corpora. Using simple and intuitive empirical observations, we derive a claim sentence query by which we are able to directly retrieve sentences in which the prior probability to include topic-relevant claims is greatly enhanced. Next, we employ simple heuristics to rank the sentences, leading to an unsupervised corpus–wide claim detection system, with precision that outperforms previously reported results on the task of claim detection given relevant documents and labeled data.
Anthology ID:
W17-5110
Volume:
Proceedings of the 4th Workshop on Argument Mining
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
ArgMining | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
79–84
Language:
URL:
https://aclanthology.org/W17-5110
DOI:
10.18653/v1/W17-5110
Bibkey:
Cite (ACL):
Ran Levy, Shai Gretz, Benjamin Sznajder, Shay Hummel, Ranit Aharonov, and Noam Slonim. 2017. Unsupervised corpus–wide claim detection. In Proceedings of the 4th Workshop on Argument Mining, pages 79–84, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Unsupervised corpus–wide claim detection (Levy et al., 2017)
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https://aclanthology.org/W17-5110.pdf
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