Abstract—Word Sense Disambiguation is a challenging technique in Natural Language Processing. There are some words in the natural languages which can cause ambiguity about the sense of the word.WSD identifies the correct sense of the word in a sentence or a document. The paper summarizes about the history of WSD. We have discussed about the knowledge - based and machine learning – based approaches for WSD. Various supervised learning and unsupervised learning techniques have been discussed. WSD is mainly used in Information Retrieval (IR), Information Extraction (IE), Machine Translation (MT), Content Analysis, Word Processing, Lexicography and Semantic Web. Finally, we have discussed about WSD for Indian languages (Hindi, Malayalam, and Kannada) and other languages (Chinese, Mongolian, Polish, Turkish, English, Myanmar, Arabic, Nepali, Persian, Dutch, and Italian).

Index Terms—word sense disambiguation (WSD), natural language processing (NLP), supervised, unsupervised, knowledge, information retrieval, information extraction, machine translation, context, ambiguity, polysemous words

Cite: Shallu and Vishal Gupta, "A Survey of Word-sense Disambiguation Effective Techniques and Methods for Indian Languages," Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 4, pp. 354-360, November 2013. doi:10.4304/jetwi.5.4.354-360