Abstract
We present our team Scubed’s approach in the 3C Citation Context Classification Task, Subtask B, citation context influence classification. Our approach relies on text based features transformed via tf-idf features followed by training a variety of simple models resulting in a strong baseline. Our best model on the leaderboard is a random forest classifier using only the citation context text. A replication of our analysis finds logistic regression and gradient boosted tree classifier to be the best performing model. Our submission code can be found at: https://github.com/napsternxg/Citation_Context_Classification.- Anthology ID:
- 2020.wosp-1.10
- Volume:
- Proceedings of the 8th International Workshop on Mining Scientific Publications
- Month:
- 05 August
- Year:
- 2020
- Address:
- Wuhan, China
- Editors:
- Petr Knoth, Christopher Stahl, Bikash Gyawali, David Pride, Suchetha N. Kunnath, Drahomira Herrmannova
- Venue:
- WOSP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 65–70
- Language:
- URL:
- https://aclanthology.org/2020.wosp-1.10
- DOI:
- Cite (ACL):
- Shubhanshu Mishra and Sudhanshu Mishra. 2020. Scubed at 3C task B - A simple baseline for citation context influence classification. In Proceedings of the 8th International Workshop on Mining Scientific Publications, pages 65–70, Wuhan, China. Association for Computational Linguistics.
- Cite (Informal):
- Scubed at 3C task B - A simple baseline for citation context influence classification (Mishra & Mishra, WOSP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.wosp-1.10.pdf
- Code
- napsternxg/citation_context_classification