DocumentCode
458863
Title
Automatic Entity Relation Extraction Based on Maximum Entropy
Author
Zhang Suxiang ; Wen Juan ; Wang Xiaojie ; Li Lei
Author_Institution
Sch. of Inf. Eng., Beijing Univ. of Posts & Telecommun.
Volume
1
fYear
2006
fDate
16-18 Oct. 2006
Firstpage
540
Lastpage
544
Abstract
Entity relation extraction (RE) is an very important research domain in information extraction, we can regard RE as a classification problem in this paper, RE is still original study field in Chinese language now, maximum entropy (ME)-based machine learning is the first time to be used to extract entity relations between named entities from Chinese texts, Thirteen features have been designed for entity relation extraction, which includes morphology, grammar and semantic feature. The system architecture for RE has been constructed. Experiment shows that the performance is promising. So it is useful for ME-based machine learning to solve RE problem
Keywords
grammars; learning (artificial intelligence); maximum entropy methods; natural language processing; text analysis; Chinese language; Chinese texts; automatic entity relation extraction; classification problem; feature selection; grammar; information extraction; machine learning; maximum entropy; morphology; semantic feature; Data mining; Design engineering; Entropy; Kernel; Learning systems; Machine learning; Machine learning algorithms; Natural language processing; Natural languages; Power engineering and energy; Maximum Entropy; entity relation extraction and evaluation; feature selection;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
Conference_Location
Jinan
Print_ISBN
0-7695-2528-8
Type
conf
DOI
10.1109/ISDA.2006.115
Filename
4021496
Link To Document