Dependency-Based Relation Mining for Biomedical Literature

Fabio Rinaldi; Gerold Schneider; Kaarel Kaljurand; Michael Hess

Dependency-Based Relation Mining for Biomedical Literature

Fabio Rinaldi, Gerold Schneider, Kaarel Kaljurand, Michael Hess

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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.

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
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Publisher:: European Language Resources Association (ELRA)
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URL:: http://www.lrec-conf.org/proceedings/lrec2008/pdf/728_paper.pdf
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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)
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PDF:: http://www.lrec-conf.org/proceedings/lrec2008/pdf/728_paper.pdf

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@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 = "https://aclanthology.org/L08-1235/",
    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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%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 https://aclanthology.org/L08-1235/

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Markdown (Informal)

[Dependency-Based Relation Mining for Biomedical Literature](https://aclanthology.org/L08-1235/) (Rinaldi et al., LREC 2008)

Dependency-Based Relation Mining for Biomedical Literature (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).