Abstract
In this paper we tackle semantic relationships extraction from medical texts. We focus on the relations that may occur between Diseases and Treatments. We propose an approach relying on two different techniques to extract the target relations: (i) relation patterns based on human expertise and (ii) machine learning based on SVM classification. This approach takes advantage of the two techniques, relying more on manual patterns when few relation samples are available and more on feature values when sufficient examples are available. Experimentations show that our approach obtains an overall 94.07% F-measure for the extraction of cure, prevent and side effect relations.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Références
Engelbrecht R. Expert systems for medicine—functions and developments. Zentralbl Gynakol 1997; 119(9): 428–34
Hotvedt M. Continuing medical education: actually learning rather than simply listening. JAMA 1996; 275(21): 1637–8
Shadow G, MacDonald C. Extracting structured information from free text pathology reports. In: AMIA Annu Symp Proc, Washington, DC. 2003
Delbecque T, Jacquemart P, Zweigenbaum P. Utilisation du réseau sémantique de l’UMLS pour la définition de types d’entités nommées médicales dans un système de questions-réponses: impact de la source des documents explorés. In: CORIA, Grenoble. CLIPS, 2005; 101–15
Embarek M, Ferret O. Learning patterns for building resources about semantic relations in the medical domain. In: LREC’08, May 2008
Hindle D. Noun classification from predicate argument structures. In: Proc. 28th Annual Meeting of the Association for Computational Linguistics (ACL’90), Berkeley, USA. 1990
Wang T, Li Y, Bontcheva K, Cunningham H, Wang J. Automatic extraction of hierarchical relations from text. In: Proceedings of the Third European Semantic Web Conference (ESWC 2006), 2006
Hearst M. Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th International Conference on Computational Linguistics (COLING-1992), 1992; 539–45
Suchanek FM, Ifrim G, Weikum G. Combining linguistic and statistical analysis to extract relations from Web documents. In: KDD ‹06›: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, April 2006
Stapley B, Benoit G. Biobibliometrics: information retrieval and visualization from co-occurrences of gene names in medline abstracts. In: Proceedings of the Pacific Symposium on Biocomputing, Hawaii, USA. 2000; 529–40
Cimino J, Barnett G. Automatic knowledge acquisition from MEDLINE. Methods Inf Med 1993; 32(2): 120–30
Khoo CSG, Chan S, Niu Y. Extracting causal knowledge from a medical database using graphical patterns. In: Proc. 38th Annual Meeting of the Association for Computational Linguistics (ACL 2000), 2000; 336–43
Thomas C. Rindflesch CAB, Sneiderman CA. Argument identification for arterial branching predications asserted in cardiac catheterization reports. In: AMIA Annu Symp Proc, 2000; 704–8
Xiao J, Su J, Zhou G, Tan C. Protein-protein interaction extraction: a supervised learning approach. In: Proceedings of the 1st International Symposium on Semantic Mining in Biomedicine (SMBM), 2005
Roberts A, Gaizauskas R, Hepple M. Extracting clinical relationships from patient narratives. In: BioNLP 2008, 2008
Joachims T. Text categorization with support vector machines: Learning with many relevant features. In: ECML-98, 10th European Conference on Machine Learning, 1998
Lee C, Khoo C, Na J. Automatic identification of treatment relations for medical ontology learning: An exploratory study. In: McIlwaine I, ed, Knowledge Organization and the Global Information Society: Proceedings of the Eighth International ISKO Conference, 2004
Ben Abacha A, Zweigenbaum P. Automatic extraction of semantic relations between medical entities: Application to the treatment relation. In: Collier N et Hahn U, eds, Proceedings of the Fourth International Symposium on Semantic Mining in Biomedicine (SMBM), Hinxton, Cambridgeshire, UK. October 2010; 4–11
Rosario B, Hearst MA. Classifying semantic relations in bioscience text. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), Barcelona, July 2004
Frunza O, Inkpen D. Extraction of disease-treatment semantic relations from biomedical sentences. In: Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, Uppsala, Sweden. Association for Computational Linguistics, July 2010; 91–8
Hall M, Frank E, Holmes G, et al. The WEKA data mining software: An update. SIGKDD Explorations 2009; 11(1)
Chang CC, Lin CJ. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag France
About this chapter
Cite this chapter
Ben Abacha, A., Zweigenbaum, P. (2011). Une approche hybride pour la détection automatique des relations sémantiques entre entités médicales. In: Staccini, P.M., Harmel, A., Darmoni, S.J., Gouider, R. (eds) Systèmes d’information pour l’amélioration de la qualité en santé. Informatique et Santé, vol 1. Springer, Paris. https://doi.org/10.1007/978-2-8178-0285-5_2
Download citation
DOI: https://doi.org/10.1007/978-2-8178-0285-5_2
Publisher Name: Springer, Paris
Print ISBN: 978-2-8178-0284-8
Online ISBN: 978-2-8178-0285-5