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A Hybrid Oriya Named Entity Recognition system: Harnessing the Power of Rule
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International Journal of Artificial Intelligence and Expert Systems (IJAE)
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Volume:  1    Issue:  1
Pages:  1-6
Publication Date:   May 2010
ISSN (Online): 2180-124X
Pages 
1 - 6
Author(s)  
Sitanath Biswas - India
S. P. Mishra - India
S Acharya - India
S Mohanty - India
 
Published Date   
01-04-2010 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
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Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
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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. 
 
 
 
1 Hai Leong Chieu and Hwee Tou Ng, Named Entity Recognition with a Maximum Entropy Approach. In: Proceedings of CoNLL-2003, Edmonton, Canada, 2003, pp.160-163.
2 Oliver Bender, Franz Josef Och and Hermann Ney, Maximum Entropy Models for Named Entity Recognition In: Proceedings of CoNLL- 2003, Edmonton, Canada, 2003 pp.148-151.
3 Bikel Daniel M., Miller Scott, Schwartz Richard and Weischedel Ralph. 1997. Nymble: A High Performance Learning Name-finder. In Proceedings of the Fifth Conference on Applied Natural Language Processing, 194– 201.
4 Borthwick Andrew. 1999. A Maximum Entropy Approach to Named Entity Recognition. Ph.D.thesis, Computer Science Department, New York University.
5 Cucerzan Silviu and Yarowsky David. 1999. Language Independent Named Entity Recognition Combining Morphological and Contextual Evidence. In Proceedings of the Joint SIGDAT Conference on EMNLP and VLC 1999, 90–99.
6 Kumarn. and Bhattacharyya Pushpak. 2006. Named Entity Recognition in Hindi using MEMM. In Technical Report, IIT Bombay,India..
7 Li Wei and McCallum Andrew. 2004. Rapid Development of Hindi Named Entity Recognition using Conditional Random Fields and Feature Induction (Short Paper).In ACM Transactions on Computational Logic.
8 McDonald R., Crammer K. and Pereira F. 2005. Flexible text segmentation with structured multilabel classification. In Proceedings of EMNLP05.
9 Srihari R., Niu C. and Li W. 2000. A Hybrid Approach for Named Entity and Sub-Type Tagging. In Proceedings of the sixth conference on Applied natural language processing.
 
 
 
 
 
 
 
 
Sitanath Biswas : Colleagues
S. P. Mishra : Colleagues
S Acharya : Colleagues
S Mohanty : Colleagues  
 
 
 
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