Abstract
This paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural network for short text categorization using multiple filter sizes. Our approach beats the baseline classifiers on different event data with good F1 scores. The best of our submitted runs achieves rank 1st among all scores on subtask B.- Anthology ID:
- S17-2081
- 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:
- 465–469
- Language:
- URL:
- https://aclanthology.org/S17-2081
- DOI:
- 10.18653/v1/S17-2081
- Cite (ACL):
- Yi-Chin Chen, Zhao-Yang Liu, and Hung-Yu Kao. 2017. IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 465–469, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification (Chen et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S17-2081.pdf