Chinese named entity recognition based on hierarchical hybrid model

Liao, Zhihua, Zhang, Zili and Liu, Yang 2010, Chinese named entity recognition based on hierarchical hybrid model, in PRICAI 2010 : trends in artificial intelligence : 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30-September 2, 2010 : proceedings, Springer, Berlin, Germany, pp.620-624.

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Title Chinese named entity recognition based on hierarchical hybrid model
Author(s) Liao, Zhihua
Zhang, Zili
Liu, Yang
Title of book PRICAI 2010 : trends in artificial intelligence : 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30-September 2, 2010 : proceedings
Editor(s) Zhang, Byoung-Tak
Orgun, Mehmet A.
Publication date 2010
Series Lecture Notes in Artificial Intelligence; v6230
Chapter number 59
Total chapters 73
Start page 620
End page 624
Total pages 5
Publisher Springer
Place of Publication Berlin, Germany
Summary Chinese named entity recognition is a challenging, difficult, yet important task in natural language processing. This paper presents a novel approach based on a hierarchical hybrid model to recognize Chinese named entities. Three mutually dependent stages-boosting, Markov Logic Networks (MLNs) based recognition, and abbreviation detection - are integrated in the model. AdaBoost algorithm is utilized for fast recognition of simple named entities first. More complex named entities are then piped into MLNs for accurate recognition. In particular, the left boundary recognition of named entities is considered. Lastly, special care is taken for classifying the abbreviated named entities by using the global context information in the same document. Experiments were conducted on People's Daily corpus. The results show that our approach can improve the performance significantly with precision of 94.38%, recall of 93.89%, and F β =1 value of 93.97%.
ISBN 3642152457
9783642152450
ISSN 0302-9743
Language eng
Field of Research 080107 Natural Language Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
HERDC collection year 2010
Copyright notice ©2010, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30033728

Document type: Book Chapter
Collection: School of Information Technology
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