Abstract
This work is implemented using the Naïve Bayes probabilistic model. The whole task is implemented in two phases. First, the algorithm was tested on a dataset from the Bengali corpus, which was developed in the TDIL (Technology Development for the Indian Languages) project of the Govt. of India. In the first execution of the algorithm, the accuracy of result was nearly 80 %. In addition to the disambiguation task, the sense evaluated sentences were inserted into the related learning sets to take part in the next executions. In the second phase, after a small manipulation over the learning sets, a new input data set was tested using the same algorithm, and in this second execution, the algorithm produced a better result, around 83 %. The results were verified with the help of a standard Bengali dictionary.
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Pal, A.R., Saha, D. (2016). Word Sense Disambiguation in Bengali: An Auto-updated Learning Set Increases the Accuracy of the Result. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 435. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2757-1_42
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