  Named Entity Recognition and Classification (NERC) is a process of
identification of proper nouns in the text and classification of those nouns
into certain predefined categories like person name, location, organization,
date, and time etc. NERC in Kannada is an essential and challenging task. The
aim of this work is to develop a novel model for NERC, based on Multinomial
Na\"ive Bayes (MNB) Classifier. The Methodology adopted in this paper is based
on feature extraction of training corpus, by using term frequency, inverse
document frequency and fitting them to a tf-idf-vectorizer. The paper discusses
the various issues in developing the proposed model. The details of
implementation and performance evaluation are discussed. The experiments are
conducted on a training corpus of size 95,170 tokens and test corpus of 5,000
tokens. It is observed that the model works with Precision, Recall and
F1-measure of 83%, 79% and 81% respectively.
