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://aclanthology.org/C18-2010
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/C18-2010.pdf