@inproceedings{rinaldi-etal-2008-dependency,
title = "Dependency-Based Relation Mining for Biomedical Literature",
author = "Rinaldi, Fabio and
Schneider, Gerold and
Kaljurand, Kaarel and
Hess, Michael",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Tapias, Daniel",
booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
month = may,
year = "2008",
address = "Marrakech, Morocco",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/728_paper.pdf",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Dependency-Based Relation Mining for Biomedical Literature
%A Rinaldi, Fabio
%A Schneider, Gerold
%A Kaljurand, Kaarel
%A Hess, Michael
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Tapias, Daniel
%S Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)
%D 2008
%8 May
%I European Language Resources Association (ELRA)
%C Marrakech, Morocco
%F rinaldi-etal-2008-dependency
%X 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.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/728_paper.pdf
Markdown (Informal)
[Dependency-Based Relation Mining for Biomedical Literature](http://www.lrec-conf.org/proceedings/lrec2008/pdf/728_paper.pdf) (Rinaldi et al., LREC 2008)
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).