Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings
Debanjan Mahata, John Kuriakose, Rajiv Ratn Shah, Roger Zimmermann
Abstract
Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.- Anthology ID:
- N18-2100
- 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:
- 634–639
- Language:
- URL:
- https://aclanthology.org/N18-2100
- DOI:
- 10.18653/v1/N18-2100
- Cite (ACL):
- Debanjan Mahata, John Kuriakose, Rajiv Ratn Shah, and Roger Zimmermann. 2018. Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings. 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 634–639, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings (Mahata et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N18-2100.pdf