Abstract
Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far underexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases.- Anthology ID:
- P17-2054
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 341–346
- Language:
- URL:
- https://aclanthology.org/P17-2054
- DOI:
- 10.18653/v1/P17-2054
- Cite (ACL):
- Isabelle Augenstein and Anders Søgaard. 2017. Multi-Task Learning of Keyphrase Boundary Classification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 341–346, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Task Learning of Keyphrase Boundary Classification (Augenstein & Søgaard, ACL 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/P17-2054.pdf