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Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval

Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval

Laura Diosan (Institut National des Sciences Appliquées, France & Babes-Bolyai University, Romania), Alexandrina Rogozan (Institut National des Sciences Appliquées, France), and Jean-Pierre Pécuchet (Institut National des Sciences Appliquées, France)
Copyright: © 2009 |Pages: 28
ISBN13: 9781605662749|ISBN10: 1605662747|ISBN13 Softcover: 9781616925284|EISBN13: 9781605662756
DOI: 10.4018/978-1-60566-274-9.ch005
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MLA

Diosan, Laura, et al. "Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval." Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, IGI Global, 2009, pp. 78-105. https://doi.org/10.4018/978-1-60566-274-9.ch005

APA

Diosan, L., Rogozan, A., & Pécuchet, J. (2009). Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval. In V. Prince & M. Roche (Eds.), Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration (pp. 78-105). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-60566-274-9.ch005

Chicago

Diosan, Laura, Alexandrina Rogozan, and Jean-Pierre Pécuchet. "Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval." In Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, 78-105. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-274-9.ch005

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Abstract

The automatic alignment between a specialized terminology used by librarians in order to index concepts and a general vocabulary employed by a neophyte user in order to retrieve medical information will certainly improve the performances of the search process, this being one of the purposes of the ANR VODEL project. The authors propose an original automatic alignment of definitions taken from different dictionaries that could be associated to the same concept although they may have different labels. The definitions are represented at different levels (lexical, semantic and syntactic), by using an original and shorter representation, which concatenates more similarities measures between definitions, instead of the classical one (as a vector of word occurrence, whose length equals the number of different words from all the dictionaries). The automatic alignment task is considered as a classification problem and three Machine Learning algorithms are utilised in order to solve it: a k Nearest Neighbour algorithm, an Evolutionary Algorithm and a Support Vector Machine algorithm. Numerical results indicate that the syntactic level of nouns seems to be the most important, determining the best performances of the SVM classifier.

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