Word sense disambiguation in Bengali: A lemmatized system increases the accuracy of the result

In the proposed approach, an attempt was made to disambiguate Bengali ambiguous words using Naïve Bayes Classification algorithm. The whole task was divided into two modules. Each module executes a specific task. In the first module, the algorithm was applied on a regular text, collected from the Bengali text corpus developed in the TDIL project of the Govt. of India and the accuracy of disambiguation process was obtained around 80%. In the second module, the whole training data and the test data were lemmatized and applying the same algorithm, around 85% accurate result was obtained. The output was verified with a previously tagged output file, generated with the help of a Bengali lexical dictionary. The implicational relevance of this study was attested in automatic text classification, machine learning, information extraction, and word sense disambiguation.