Multi-Task Learning of Keyphrase Boundary Classification

Isabelle Augenstein, Anders Søgaard


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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/P17-2054.pdf