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Named Entity Recognition and Classification (NERC) is a process ofidentification of proper nouns in the text and classification of those nounsinto certain predefined categories like person name, location, organization,date, and time etc. NERC in Kannada is an essential and challenging task. Theaim of this work is to develop a novel model for NERC, based on MultinomialNa\"ive Bayes (MNB) Classifier. The Methodology adopted in this paper is basedon feature extraction of training corpus, by using term frequency, inversedocument frequency and fitting them to a tf-idf-vectorizer. The paper discussesthe various issues in developing the proposed model. The details ofimplementation and performance evaluation are discussed. The experiments areconducted on a training corpus of size 95,170 tokens and test corpus of 5,000tokens. It is observed that the model works with Precision, Recall andF1-measure of 83%, 79% and 81% respectively.

Kannada named entity recognition and classification (nerc) based on multinomial na\"ive bayes (mnb) classifier