  In this paper, we describe a research method that generates Bangla word
clusters on the basis of relating to meaning in language and contextual
similarity. The importance of word clustering is in parts of speech (POS)
tagging, word sense disambiguation, text classification, recommender system,
spell checker, grammar checker, knowledge discover and for many others Natural
Language Processing (NLP) applications. In the history of word clustering,
English and some other languages have already implemented some methods on word
clustering efficiently. But due to lack of the resources, word clustering in
Bangla has not been still implemented efficiently. Presently, its
implementation is in the beginning stage. In some research of word clustering
in English based on preceding and next five words of a key word they found an
efficient result. Now, we are trying to implement the tri-gram, 4-gram and
5-gram model of word clustering for Bangla to observe which one is the best
among them. We have started our research with quite a large corpus of
approximate 1 lakh Bangla words. We are using a machine learning technique in
this research. We will generate word clusters and analyze the clusters by
testing some different threshold values.
