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A Learning Approach for Word Sense Disambiguation in the Biomedical Domain
 

Summary: A Learning Approach for Word Sense Disambiguation
in the Biomedical Domain
Hisham Al-Mubaid*
University of Houston-Clear Lake
Houston, TX, 77058, USA
hisham@uhcl.edu
Sandeep Gungu
University of Houston-Clear Lake
Houston, TX, 77058, USA
gungus@uhcl.edu
Abstract
Word sense disambiguation, WSD, task has been investigated extensively within the natural language
processing domain. In the biomedical domain, word sense ambiguity is more widely spread with
bioinformatics research effort devoted to it is not commensurate and is allowing for more
development. In this paper, we present and evaluate a machine learning based approach for WSD. The
main limitation with supervised methods is the requirement for manually disambiguated instances of
the ambiguous word to be used for training. However, the advances in automatic text annotation and
tagging techniques with the help of the plethora of knowledge sources like ontologies and text
literature in the biomedical domain will help lessen this limitation. Our approach has been evaluated
with the benchmark dataset NLM-WSD with three settings. The accuracy results showed that our

  

Source: Al-Mubaid, Hisham - School of Science and Computer Engineering, University of Houston-Clear Lake

 

Collections: Computer Technologies and Information Sciences