Abstract
This paper presents an exhaustive study on the generation of graph input to unsupervised graph-based non-contextual single document keyword extraction systems. A concrete hypothesis on concept coordination for documents that are scientific articles is put forward, consistent with two separate graph models : one which is based on word adjacency in the linear text–an approach forming the foundation of all previous graph-based keyword extraction methods, and a novel one that is based on word adjacency modulo their modifiers. In doing so, we achieve a best reported NDCG score to date of 0.431 for any system on the same data. In terms of a best parameter f-score, we achieve the highest reported to date (0.714) at a reasonable ranked list cut-off of n = 6, which is also the best reported f-score for any keyword extraction or generation system in the literature on the same data. The best-parameter f-score corresponds to a reduction in error of 12.6% conservatively.- Anthology ID:
- 2015.jeptalnrecital-court.10
- Volume:
- Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts
- Month:
- June
- Year:
- 2015
- Address:
- Caen, France
- Editors:
- Jean-Marc Lecarpentier, Nadine Lucas
- Venue:
- JEP/TALN/RECITAL
- SIG:
- Publisher:
- ATALA
- Note:
- Pages:
- 61–67
- Language:
- URL:
- https://aclanthology.org/2015.jeptalnrecital-court.10
- DOI:
- Cite (ACL):
- Natalie Schluter. 2015. Effects of Graph Generation for Unsupervised Non-Contextual Single Document Keyword Extraction. In Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts, pages 61–67, Caen, France. ATALA.
- Cite (Informal):
- Effects of Graph Generation for Unsupervised Non-Contextual Single Document Keyword Extraction (Schluter, JEP/TALN/RECITAL 2015)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2015.jeptalnrecital-court.10.pdf