Summary of the paper
| Title | Dependency-Based Relation Mining for Biomedical Literature |
|---|---|
| Authors | Fabio Rinaldi, Gerold Schneider, Kaarel Kaljurand and 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. |
| Language | Single language |
| Topics | Text mining, Information Extraction, Information Retrieval, Parsing Systems |
Full paper |
Dependency-Based Relation Mining for Biomedical Literature |
Slides |
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| Bibtex | @InProceedings{RINALDI08.728, author = {Fabio Rinaldi, Gerold Schneider, Kaarel Kaljurand and Michael Hess}, title = {Dependency-Based Relation Mining for Biomedical Literature}, booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, year = {2008}, month = {may}, date = {28-30}, address = {Marrakech, Morocco}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-4-0}, note = {http://www.lrec-conf.org/proceedings/lrec2008/}, language = {english} } |