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

arXiv: https://arxiv.org/abs/1509.04385 · HTML: https://mlsanity.com/paper/1509.04385/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [Predicting Gender by First Name Using Character-level Machine Learning](https://mlsanity.com/paper/2106.10156/) (2021)

## Abstract (arXiv)

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.