Using Machine Learning Algorithms for Word Sense Disambiguation: A Brief Survey

In the entire vocabulary of Human language, numerous words have more than one distinct meaning and thus present a contextual ambiguity which is a worth of one of the many language based problems needs procedure based resolution. Approaches to WSD are often classified according to the main source of knowledge used in sense differentiation. Methods that rely primarily on dictionaries, thesauri, and lexical knowledge bases, without using any corpus evidence, are termed dictionary-based or knowledge based. Natural language tends to be ambiguous. Comparing and evaluating different WSD systems is extremely difficult, because of the different test sets, sense inventories, and knowledge resources adopted. In this research we shall address the problem of Word Sense Disambiguation by a combination of learning algorithms. The study is aimed at comparing the performance of using machine learning algorithms for Word Sense Disambiguation (WSD)