Word sense disambiguation in Bengali: A lemmatized system increases the accuracy of the result
Alok Ranjan Pal, Diganta Saha, Sudip Kumar Naskar, Niladri Sekhar Dash · 2015
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.