Unsupervised Keyphrase Extraction with Multipartite Graphs

Florian Boudin


Abstract
We propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure. Our model represents keyphrase candidates and topics in a single graph and exploits their mutually reinforcing relationship to improve candidate ranking. We further introduce a novel mechanism to incorporate keyphrase selection preferences into the model. Experiments conducted on three widely used datasets show significant improvements over state-of-the-art graph-based models.
Anthology ID:
N18-2105
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
667–672
Language:
URL:
https://aclanthology.org/N18-2105
DOI:
10.18653/v1/N18-2105
Bibkey:
Cite (ACL):
Florian Boudin. 2018. Unsupervised Keyphrase Extraction with Multipartite Graphs. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 667–672, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Keyphrase Extraction with Multipartite Graphs (Boudin, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-2105.pdf
Note:
 N18-2105.Notes.pdf
Code
 boudinfl/pke