CRST: a Claim Retrieval System in Twitter

Wenjia Ma, WenHan Chao, Zhunchen Luo, Xin Jiang


Abstract
For controversial topics, collecting argumentation-containing tweets which tend to be more convincing will help researchers analyze public opinions. Meanwhile, claim is the heart of argumentation. Hence, we present the first real-time claim retrieval system CRST that retrieves tweets containing claims for a given topic from Twitter. We propose a claim-oriented ranking module which can be divided into the offline topic-independent learning to rank model and the online topic-dependent lexicon model. Our system outperforms previous claim retrieval system and argument mining system. Moreover, the claim-oriented ranking module can be easily adapted to new topics without any manual process or external information, guaranteeing the practicability of our system.
Anthology ID:
C18-2010
Volume:
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico
Editor:
Dongyan Zhao
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
43–47
Language:
URL:
https://aclanthology.org/C18-2010
DOI:
Bibkey:
Cite (ACL):
Wenjia Ma, WenHan Chao, Zhunchen Luo, and Xin Jiang. 2018. CRST: a Claim Retrieval System in Twitter. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 43–47, Santa Fe, New Mexico. Association for Computational Linguistics.
Cite (Informal):
CRST: a Claim Retrieval System in Twitter (Ma et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-2010.pdf