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Based on the sense definition of words available in the Bengali WordNet, anattempt is made to classify the Bengali sentences automatically into differentgroups in accordance with their underlying senses. The input sentences arecollected from 50 different categories of the Bengali text corpus developed inthe TDIL project of the Govt. of India, while information about the differentsenses of particular ambiguous lexical item is collected from Bengali WordNet.In an experimental basis we have used Naive Bayes probabilistic model as auseful classifier of sentences. We have applied the algorithm over 1747sentences that contain a particular Bengali lexical item which, because of itsambiguous nature, is able to trigger different senses that render sentences indifferent meanings. In our experiment we have achieved around 84% accurateresult on the sense classification over the total input sentences. We haveanalyzed those residual sentences that did not comply with our experiment anddid affect the results to note that in many cases, wrong syntactic structuresand less semantic information are the main hurdles in semantic classificationof sentences. The applicational relevance of this study is attested inautomatic text classification, machine learning, information extraction, andword sense disambiguation.

Automatic classification of bengali sentences based on sense definitions present in bengali wordnet