@inproceedings{ma-etal-2018-crst,
title = "{CRST}: a Claim Retrieval System in {T}witter",
author = "Ma, Wenjia and
Chao, WenHan and
Luo, Zhunchen and
Jiang, Xin",
editor = "Zhao, Dongyan",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-2010/",
pages = "43--47",
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."
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%0 Conference Proceedings
%T CRST: a Claim Retrieval System in Twitter
%A Ma, Wenjia
%A Chao, WenHan
%A Luo, Zhunchen
%A Jiang, Xin
%Y Zhao, Dongyan
%S Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F ma-etal-2018-crst
%X 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.
%U https://aclanthology.org/C18-2010/
%P 43-47
Markdown (Informal)
[CRST: a Claim Retrieval System in Twitter](https://aclanthology.org/C18-2010/) (Ma et al., COLING 2018)
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.