@inproceedings{levy-etal-2017-unsupervised,
title = "Unsupervised corpus{--}wide claim detection",
author = "Levy, Ran and
Gretz, Shai and
Sznajder, Benjamin and
Hummel, Shay and
Aharonov, Ranit and
Slonim, Noam",
editor = "Habernal, Ivan and
Gurevych, Iryna and
Ashley, Kevin and
Cardie, Claire and
Green, Nancy and
Litman, Diane and
Petasis, Georgios and
Reed, Chris and
Slonim, Noam and
Walker, Vern",
booktitle = "Proceedings of the 4th Workshop on Argument Mining",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5110",
doi = "10.18653/v1/W17-5110",
pages = "79--84",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Unsupervised corpus–wide claim detection
%A Levy, Ran
%A Gretz, Shai
%A Sznajder, Benjamin
%A Hummel, Shay
%A Aharonov, Ranit
%A Slonim, Noam
%Y Habernal, Ivan
%Y Gurevych, Iryna
%Y Ashley, Kevin
%Y Cardie, Claire
%Y Green, Nancy
%Y Litman, Diane
%Y Petasis, Georgios
%Y Reed, Chris
%Y Slonim, Noam
%Y Walker, Vern
%S Proceedings of the 4th Workshop on Argument Mining
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F levy-etal-2017-unsupervised
%X 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.
%R 10.18653/v1/W17-5110
%U https://aclanthology.org/W17-5110
%U https://doi.org/10.18653/v1/W17-5110
%P 79-84
Markdown (Informal)
[Unsupervised corpus–wide claim detection](https://aclanthology.org/W17-5110) (Levy et al., ArgMining 2017)
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.