Abstract
We present our team Scubed’s approach in the ‘3C’ Citation Context Classification Task, Subtask A, citation context purpose classification. Our approach relies on text based features transformed via tf-idf features followed by training a variety of models which are capable of capturing non-linear features. Our best model on the leaderboard is a multi-layer perceptron which also performs best during our rerun. Our submission code for replicating experiments is at: https://github.com/napsternxg/Citation_Context_Classification.- Anthology ID:
- 2020.wosp-1.9
- 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:
- 59–64
- Language:
- URL:
- https://aclanthology.org/2020.wosp-1.9
- DOI:
- Cite (ACL):
- Shubhanshu Mishra and Sudhanshu Mishra. 2020. Scubed at 3C task A - A simple baseline for citation context purpose classification. In Proceedings of the 8th International Workshop on Mining Scientific Publications, pages 59–64, Wuhan, China. Association for Computational Linguistics.
- Cite (Informal):
- Scubed at 3C task A - A simple baseline for citation context purpose classification (Mishra & Mishra, WOSP 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.wosp-1.9.pdf
- Code
- napsternxg/citation_context_classification