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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N18-2105.pdf
- Code
- boudinfl/pke