Abstract
This paper describes our submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours. Given a rumoured tweet and a lot of reply tweets, the subtask A is to label whether these tweets are support, deny, query or comment, and the subtask B aims to predict the veracity (i.e., true, false, and unverified) with a confidence (in range of 0-1) of the given rumoured tweet. For both subtasks, we adopted supervised machine learning methods, incorporating rich features. Since training data is imbalanced, we specifically designed a two-step classifier to address subtask A .- Anthology ID:
- S17-2086
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 491–496
- Language:
- URL:
- https://aclanthology.org/S17-2086
- DOI:
- 10.18653/v1/S17-2086
- Cite (ACL):
- Feixiang Wang, Man Lan, and Yuanbin Wu. 2017. ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 491–496, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models (Wang et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/S17-2086.pdf