DocumentCode :
476212
Title :
Research on Me-based Chinese NER model
Author :
Zhang, Yue-jie ; Zhang, Tao
Author_Institution :
Dept. of Comput. Sci. & Eng., Fudan Univ., Shanghai
Volume :
5
fYear :
2008
fDate :
12-15 July 2008
Firstpage :
2597
Lastpage :
2602
Abstract :
This paper presents a hybrid pattern for Chinese Name Entity Recognition based on Maximum Entropy model. Firstly, Maximum Entropy model is an outstanding statistical model for its good integration of various constraints and its compatibility to Chinese Named Entity Recognition. Secondly, local features and global features are integrated in the hybrid model to get high performance. Thirdly, in order to reduce the searching space and improve the processing efficiency, heuristic human knowledge is introduced into the model, which could increase the recognition performance significantly. From the experimental results on Peoplepsilas Daily corpus, it can be observed that the hybrid model is an effective pattern to combine statistical model and heuristic human knowledge.
Keywords :
maximum entropy methods; natural language processing; pattern recognition; statistical analysis; Chinese name entity recognition; People daily corpus; heuristic human knowledge; hybrid pattern; maximum entropy model; searching space; Computer science; Cybernetics; Data mining; Dictionaries; Entropy; Feature extraction; Humans; Laboratories; Machine learning; Probability distribution; Global feature; Heuristic human knowledge; Local feature; Maximum entropy model; Named entity recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
Type :
conf
DOI :
10.1109/ICMLC.2008.4620846
Filename :
4620846
Link To Document :
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