Performance comparison of Word sense disambiguation approaches for Indian languages

Abstract

Natural Language Processing (NLP) involves many phases of which the significant one is Word-sense disambiguation (WSD). WSD includes the techniques of identifying a suitable meaning of words and sentences in a particular context by applying various computational procedures. WSD is an Artificial Intelligence problem that needs resolution for ambiguity of words. WSD is essential for many NLP applications like Machine Translation, Information Retrieval, Information Extraction and for many others. The WSD techniques are mainly categorized into knowledge-based approaches, Machine Learning based approaches and hybrid approaches. The assessment of WSD systems is discussed in this study and it includes comparisons of different WSD approaches in the context of Indian languages.