A Hybrid Oriya Named Entity Recognition system: Harnessing the Power of Rule

This paper describes a hybrid system that applies maximum entropy (MaxEnt) model with Hidden Markov model (HMM) and some linguistic rules to recognize name entities in Oriya language. The main advantage of our system is, we are using both HMM and MaxEnt model successively with some manually developed linguistic rules. First we are using MaxEnt to identify name entities in Oria corpus, then tagging them temporary as reference. The tagged corpus of MaxEnt now regarded as a training process for HMM. Now we use HMM for final tagging. Our approach can achieve higher precision and recall, when providing enough training data and appropriate error correction mechanism.