Named Entity Recognition for Telugu News Articles using Naïve Bayes Classifier

The Named Entity Recognition (NER) is identifying name of Person, Location, Organization etc. in a given sentence or a document. In this paper, we have attempted to classify textual content from on-line Telugu newspapers using well known generative model. We have used generic features like contextual words and their part-of-speech (POS) to build the learning model. By understanding the syntax and grammar of Telugu language, we propose morphological pre-processing of the data and this step yields us better accuracy. We propose some interesting language dependent features like post-position feature, clue word feature and gazetteer feature to improve the performance of the model. The model achieved an overall average F1-Score of 88.87% for Person, 87.32% for Location and 72.69% for Organization.