Dependency-Based Relation Mining for Biomedical Literature
Fabio Rinaldi, Gerold Schneider, Kaarel Kaljurand, Michael Hess
Abstract
We describe techniques for the automatic detection of relationships among domain entities (e.g. genes, proteins, diseases) mentioned in the biomedical literature. Our approach is based on the adaptive selection of candidate interactions sentences, which are then parsed using our own dependency parser. Specific syntax-based filters are used to limit the number of possible candidate interacting pairs. The approach has been implemented as a demonstrator over a corpus of 2000 richly annotated MedLine abstracts, and later tested by participation to a text mining competition. In both cases, the results obtained have proved the adequacy of the proposed approach to the task of interaction detection.- Anthology ID:
- L08-1235
- Volume:
- Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
- Month:
- May
- Year:
- 2008
- Address:
- Marrakech, Morocco
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/728_paper.pdf
- DOI:
- Cite (ACL):
- Fabio Rinaldi, Gerold Schneider, Kaarel Kaljurand, and Michael Hess. 2008. Dependency-Based Relation Mining for Biomedical Literature. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).
- Cite (Informal):
- Dependency-Based Relation Mining for Biomedical Literature (Rinaldi et al., LREC 2008)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/728_paper.pdf