Abstract
AbstractAnalysis of textual data helps to understand the perception of people by studying the various senses of words in the text. The sense of a polysemous word varies as per the context in the sentence. The technique for determining the correct interpretation of a polysemous word according to context is called as Word Sense Disambiguation (WSD). Recently, researchers have proposed many algorithms to solve this linguistic ambiguity problem in different languages. In this paper, we give a general summary of current trends in WSD in terms of automation of disambiguation approaches. We also mention the challenges and future directions for research for WSD systems. We also propose a system based on these future directions for research, which may increase the accuracy of WSD system for Indian languages.KeywordsSupervised learningWord Sense Disambiguation (WSD)Unsupervised learningKnowledge-basedMachine LearningSemi-supervised learningWordNetNatural Language Processing (NLP)
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