Abstract
This paper aims to develop an automatic text categorization system that classifies Bangla medical and non-medical text documents based on two primary features, that is, word length and the presence of English equivalent words in the text documents. To start with, it has been shown that based on the word length and the number of English equivalent words present in a particular text, Bangla medical text documents can be identified among other text documents of any domain. SGD (Stochastic Gradient Descent) classification algorithm is used and an accuracy of 97.75% has been achieved. Comparisons have also been done with other commonly used classifiers to test the system from which it has been observed that SGD performs better than those classifiers.
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One of the authors would like to thank Department of Science and Technology (DST) for support in the form of INSPIRE fellowship.
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Dhar, A., Dash, N.S., Roy, K. (2019). Categorization of Bangla Medical Text Documents Based on Hybrid Internal Feature. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1031. Springer, Singapore. https://doi.org/10.1007/978-981-13-8581-0_15
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DOI: https://doi.org/10.1007/978-981-13-8581-0_15
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