Categorization of Bangla Medical Text Documents Based on Hybrid Internal Feature

• Published in: Computational Intelligence, Communications, and Business Analytics Vol. 1031; pp. 181 - 192
• Main Authors: Dhar, Ankita, Dash, Niladri Sekhar, Roy, Kaushik
• Format: Book Chapter
• Language: English
• Published: Singapore Springer Singapore Pte. Limited 2019; Springer Singapore
• Series: Communications in Computer and Information Science
• Subjects: Bangla text categorization; English equivalent terms; Medical text; SGD; Word length
• ISBN: 9789811385803; 9811385807
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-981-13-8581-0_15

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.