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
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
43–47
Language:
URL:
https://www.aclweb.org/anthology/C18-2010
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C18-2010.pdf