A MAXIMUM ENTROPY MODEL FOR NAMED ENTITY RECOGNITION IN TELUGU LANGUAGE

Abstract

Named Entity Recognition (NER) is used in many applications like text summarization, text classification, question answering and machine translation systems etc..NER is the task of identifying and classifying named entities into some predefine categories like person, location, organization etc For English a lot of work has already been done in the field of NER, where capitalization is a major key for rules, whereas Indian languages do not have such feature. This makes the task difficult for Indian Languages. This work reports about the evaluation of a Named Entity Recognition (NER) system for Telugu language using the Maximum Entropy Approach (MAXENT). A MAXENT based NER system for Telugu has reported an overall Precision, Recall and F-Score values of 90.92%, 72.30% and 80.55% respectively with feature set context word, Part of Speech (POS) information, NE tag of previous word and First name Gazetteer list. A manually tagged Telugu news corpus is used for the evaluation which was developed from Telugu newspaper available online. The training set annotated with a NE tagset of 12 tags is used.