An Efficient Method for Biomedical Word Sense Disambiguation Based on Web-Kernel Similarity

Mohammed Rais, Mohammed Bekkali and Abdelmonaime Lachkar
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Mohammed Rais: LISA, Department of Electrical and Computer Engineering, ENSA, Sidi Mohamed Ben Abdellah University, Morocco
Mohammed Bekkali: LISA, Department of Electrical and Computer Engineering, ENSA, Sidi Mohamed Ben Abdellah University, Morocco
Abdelmonaime Lachkar: Systems and Data Engineering Team, Department of Information System and Communication, ENSA, Abdelmalek Essaâdi University, Morocco

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2021, vol. 16, issue 4, 1-14

Abstract: Searching for the best sense for a polysemous word remains one of the greatest challenges in the representation of biomedical text. To this end, Word Sense Disambiguation (WSD) algorithms mostly rely on an External Source of Knowledge, like a Thesaurus or Ontology, for automatically selecting the proper concept of an ambiguous term in a given Window of Context using semantic similarity and relatedness measures. In this paper, we propose a Web-based Kernel function for measuring the semantic relatedness between concepts to disambiguate an expression versus multiple possible concepts. This measure uses the large volume of documents returned by PubMed Search engine to determine the greater context for a biomedical short text through a new term weighting scheme based on Rough Set Theory (RST). To illustrate the efficiency of our proposed method, we evaluate a WSD algorithm based on this measure on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. The obtained results demonstrate promising improvements.

Date: 2021
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