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2012 (Vol. 4, Issue: 20)
Article Information:

Named Entity Recognition Based on A Machine Learning Model

Jing Wang, Zhijing Liu and Hui Zhao
Corresponding Author:  Jing Wang 

Key words:  Entity identification , Hidden Markov Model (HMM), named entity, , , ,
Vol. 4 , (20): 3973-3980
Submitted Accepted Published
December 20, 2011 April 20, 2012 October 15, 2012

For the recruitment information in Web pages, a novel unified model for named entity recognition is proposed in this study. The models provide a simple statistical framework to incorporate a wide variety of linguistic knowledge and statistical models in a unified way. In our approach, firstly, Multi-Rules are built for a better representation of the named entity, in order to emphasize the specific semantics and term space in the named entity. Then an optimal algorithm of the hierarchically structured DSTCRFs is performed, in order to pick out the structure attributes of the named entity from the recruitment knowledge and optimize the efficiency of the training. The experimental results showed that the accuracy rate has been significantly improved and the complexity of sample training has been decreased.
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  Cite this Reference:
Jing Wang, Zhijing Liu and Hui Zhao, 2012. Named Entity Recognition Based on A Machine Learning Model.  Research Journal of Applied Sciences, Engineering and Technology, 4(20): 3973-3980.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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