Enriching WordNet for Word Sense Disambiguation

In computational linguistics, word-sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying which sense of a word (i.e. meaning) is used in a sentence, when the word has multiple meanings. Research has progressed steadily to the point where WSD systems achieve sufficiently high levels of accuracy on a variety of word types and ambiguities. A rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date. The senses of a word are expressed by its WordNet synsets, arranged according to their relevance. The relevance of these senses are probabilistically determined through a Bayesian Belief Network. The main contribution of the work is a completely probabilistic framework for word-sense disambiguation with a semi-supervised learning technique utilising WordNet. Word sense disambiguation is a major problem in many tasks related to natural language processing. This paper aims to enriching wordnet for word sense disambiguation by adding some extra features to wordnet that may enhance the efficiency of knowledge-based contextual overlap WSD algorithms when they are used on wordnets.