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