Survey and Gap Analysis of Word Sense Disambiguation Approaches on Unstructured Texts

Abstract

Word Sense Disambiguation (WSD) is considered as one of the pivotal problems of Semantic classification among polysemous words that can be addressed using Natural Language Processing (NLP) for identifying the sense of the ambiguous word in a particular context. The application areas of WSD pertain to machine translation, information extraction and retrieval (IE-IR), dialogue systems, and automatic summarization kind of NLP solutions. This paper presents a survey on WSD approaches in major AI-NLP methods by comparing different approaches for WSD in supervised, unsupervised, and knowledge based algorithms. This paper also aims at providing gap analysis in surveyed WSD systems comparing strengths and weaknesses of various surveyed systems and their accuracy. Based on the findings, a future hybrid approach synergizing rule-based and machine learning based methods are contemplated. The findings of this survey are envisaged through an ongoing research on WSD based Meta-Search algorithm under C-DAC purview for an Intelligent NLP based system to detect the actual sense of search queries and providing semantic classification of news headlines and snippets containing ambiguous words.