Hierarchical clustering technique for word sense disambiguation using Hindi WordNet

Abstract

Word Sense Disambiguation (WSD) is crucial and its significance is prominent in every application of computational linguistics. WSD is a challenging problem of Natural Language Processing (NLP). Though there are lots of algorithms for WSD available, still little work is carried out for choosing optimal algorithm for that. Three approaches are available for WSD, namely, Knowledge-based approach, Supervised approach and Unsupervised approach. Also, one can use the combination of given approaches. Supervised approach needs large amounts of manually created sense-annotated corpus which takes computationally more amount of time and effort. Knowledge-based approach requires machine readable dictionaries, sense inventories, thesauri, etc, which are dependent on own interpretation about word's sense; Whereas unsupervised approach uses sense-unannotated corpus and it is based on the phenomenon of working that words that co-occur have similarity. This research is for Hindi language which uses Hierarchical clustering algorithm with different similarity measures which are cosine, Jaccard and dice, the result of clusters is overlapped with Hindi WordNet a product of IIT Bombay which improves result of word sense disambiguation as clustering does grouping of words which are similar.