An Approach to Speed-up the Word Sense Disambiguation Procedure through Sense Filtering

Abstract: In this paper, we are going to focus on speed up of the Word Sense Disambiguation procedure by filtering the relevant senses of an ambiguous word through Part-of-Speech Tagging. First, this proposed approach performs the Part-of-Speech Tagging operation before the disambiguation procedure using Bigram approximation. As a result, the exact Part-of-Speech of the ambiguous word at a particular text instance is derived. In the next stage, only those dictionary definitions (glosses) are retrieved from an online dictionary, which are associated with that particular Part-of-Speech to disambiguate the exact sense of the ambiguous word. In the training phase, we have used Brown Corpus for Part-of-Speech Tagging and WordNet as an online dictionary. The proposed approach reduces the execution time upto half (approximately) of the normal execution time for a text, containing around 200 sentences. Not only that, we have found several instances, where the correct sense of an ambiguous word is found for using the Part-of-Speech Tagging before the Disambiguation procedure.