Abstract
Today, there is a great demand for extracting useful information and ability to take actionable insights from heaps of raw textual data. The processing of regional language texts, notably with low resources is challenging. Especially for Indian Regional Languages (IRL), language text processing is in huge requirement with regard to the applications of natural language processing (NLP) and text analytics. As a part of regional language processing and text analytics text documents classification for IRL are yet to be explored. Kannada is one of the official Indian regional languages. In this paper, the new benchmark Kannada document’s dataset is created and analyzed using machine learning algorithms. This paper proposes an explicit Unicode term encoding based Kannada document classification, using the vector space model. Both term frequency (TF) and term frequency-inverse document frequency (TF-IDF), statistical measures are used for classification of Kannada documents using K-NN and SVM classifiers. SVM (linear) classifier performs better than the K-NN classifier in classifying the Kannada documents with 98.67% mean accuracy over K-Fold experiments.
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Acknowledgements
This work is supported by Vision Group on Science and Technology (VGST), Department of IT, BT and Science and Technology, Government of Karnataka, India [File No.: VGST/2019-20/GRD No.:850/397].
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Kasturi Rangan, R., Harish, B.S. (2022). Kannada Document Classification Using Unicode Term Encoding Over Vector Space. In: Shetty D., P., Shetty, S. (eds) Recent Advances in Artificial Intelligence and Data Engineering. Advances in Intelligent Systems and Computing, vol 1386. Springer, Singapore. https://doi.org/10.1007/978-981-16-3342-3_31
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DOI: https://doi.org/10.1007/978-981-16-3342-3_31
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