An Improved Approach for Word Ambiguity Removal

TL;DR

This paper presents an improved method for word sense disambiguation that combines supervised and unsupervised techniques using POS tagging and domain information to enhance accuracy.

Contribution

It introduces a novel approach integrating POS tagging with domain-based WSD, combining supervised and unsupervised methods for better ambiguity resolution.

Findings

Enhanced accuracy in word sense disambiguation

Effective domain-specific sense identification

Improved interaction between humans and computers

Abstract

Word ambiguity removal is a task of removing ambiguity from a word, i.e. correct sense of word is identified from ambiguous sentences. This paper describes a model that uses Part of Speech tagger and three categories for word sense disambiguation (WSD). Human Computer Interaction is very needful to improve interactions between users and computers. For this, the Supervised and Unsupervised methods are combined. The WSD algorithm is used to find the efficient and accurate sense of a word based on domain information. The accuracy of this work is evaluated with the aim of finding best suitable domain of word.