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

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

Mohammed Rais, Mohammed Bekkali, Abdelmonaime Lachkar
Copyright: © 2021 |Pages: 14
DOI: 10.4018/IJHISI.20211001.oa9
Article PDF Download
Open access articles are freely available for download

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.
Article Preview
Top

There are many proposed approaches which attempt to address the problem of WSD. Existing methods can be classified into three categories: Supervised (Zhong and Ng, 2010), Unsupervised (Brody and Lapata, 2009) and Knowledge-Based methods (Navigli, 2011).

Complete Article List

Search this Journal:
Reset
Volume 19: 1 Issue (2024)
Volume 18: 1 Issue (2023)
Volume 17: 2 Issues (2022)
Volume 16: 4 Issues (2021)
Volume 15: 4 Issues (2020)
Volume 14: 4 Issues (2019)
Volume 13: 4 Issues (2018)
Volume 12: 4 Issues (2017)
Volume 11: 4 Issues (2016)
Volume 10: 4 Issues (2015)
Volume 9: 4 Issues (2014)
Volume 8: 4 Issues (2013)
Volume 7: 4 Issues (2012)
Volume 6: 4 Issues (2011)
Volume 5: 4 Issues (2010)
Volume 4: 4 Issues (2009)
Volume 3: 4 Issues (2008)
Volume 2: 4 Issues (2007)
Volume 1: 4 Issues (2006)
View Complete Journal Contents Listing