Word Sense Disambiguation in Hindi Language Using Hyperspace Analogue to Language and Fuzzy C-Means Clustering

The problem of Word Sense Disambiguation (WSD) can be defined as the task of assigning the most appropriate sense to the polysemous word within a given context. Many supervised, unsupervised and semi-supervised approaches have been devised to deal with this problem, particularly, for the English language. However, this is not the case for Hindi language, where not much work has been done. In this paper, a new approach has been developed to perform disambiguation in Hindi language. For training the system, the text in Hindi language is converted into Hyperspace Analogue to Language (HAL) vectors, thereby, mapping each word into a high-dimensional space. We also deal with the fuzziness involved in disambiguation of words. We apply Fuzzy C-Means Clustering algorithm to form clusters denoting the various contexts in which the polysemous word may occur. The test data is then mapped into the high dimensional space created during the training phase. We test our approach on the corpus created using Hindi news articles and Wikipedia. We compare our approach with other significant approaches available in the literature and the experimental results indicate that our approach outperforms all the previous works done for Hindi Language.