|
Abstract
|
Article Information:
Named Entity Recognition Based on A Machine Learning Model
Jing Wang, Zhijing Liu and Hui Zhao
Corresponding Author: Jing Wang
Submitted: December 20, 2011
Accepted: April 20, 2012
Published: October 15, 2012 |
Abstract:
|
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.
Key words: Entity identification , Hidden Markov Model (HMM), named entity, , , ,
|
|
Abstract
|
PDF
|
HTML |
|
Cite this Reference:
Jing Wang, Zhijing Liu and Hui Zhao, . Named Entity Recognition Based on A Machine Learning Model. Research Journal of Applied Sciences, Engineering and Technology, (20): 3973-3980.
|
|
|
|
 |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
 |
| Information |
|
|
|
|
| Sales & Services |
|
|
|
|
