DocumentCode
2338455
Title
High precision English base noun phrase identification based on "waterfall" model
Author
Liang, Ying-Hong ; Zhao, Tie-jun ; Yu, Hao ; Yao, Jian-Min
Author_Institution
MOE-MS Key Lab. of Natural Language Process. & Speech, Harbin Inst. of Technol., China
Volume
8
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
4902
Abstract
Based on classical model that used by software exploitation in the subject of Software Engineering - waterfall model, a high precision model for English noun phrase identification is presented. In this model, three important features (interior structure, context information and boundary character) in base noun phrase identification are orderly used, and the relative method (the rule method, the transfer-based and error-driven method, and the boundary statistic method) are used from top to bottom. Thus the precision of base noun phrase identification improved steadily. The "waterfall" model combined the rule method and the statistical method so as to make them complement each other. The precision of base noun phrase identification achieves 98.10% and the F score is 95.25%. Compared to other method, our method achieves the highest precision and F score.
Keywords
computational linguistics; natural languages; software engineering; Software Engineering waterfall model; boundary character; boundary statistic method; context information; error-driven method; high precision English base noun phrase identification; interior structure; probability metric; rule method; transfer-based method; Context modeling; Electronic mail; Forestry; Laboratories; Natural language processing; Probability; Software engineering; Speech processing; Statistical analysis; Text processing; Noun phrase; probability metric; transfer rule; waterfall model;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527806
Filename
1527806
Link To Document