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A Hybrid Oriya Named Entity Recognition system: Harnessing the Power of Rule
Sitanath Biswas, S. P. Mishra, S Acharya, S Mohanty
Pages - 1 - 6     |    Revised - 28-02-2010     |    Published - 01-04-2010
Volume - 1   Issue - 1    |    Publication Date - May 2010  Table of Contents
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 Oriya corpus, and 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.
CITED BY (12)  
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6 Eboña, K. M. L., Llorca Jr, O. S., Perez, G. P., Roldan, J. M., Domingo, I. V. R., & Sagum, R. A. (2013). Named-Entity Recognizer (NER) for Filipino Novel Excerpts using Maximum Entropy Approach. Journal of Industrial and Intelligent Information Vol, 1(1).
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Mr. Sitanath Biswas
- India
Mr. S. P. Mishra
- India
Mr. S Acharya
- India
Mr. S Mohanty
- India

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