Title :
Combining Syntactic Structured and Flat Features for Relation Extraction Using Co-training
Author :
Qiu, Jing ; Liao, Lejian ; Li, Peng
Author_Institution :
Beijing Lab. of Intell. Inf. Technol., Beijing Inst. of Technol., Beijing, China
Abstract :
A parse tree contains rich syntactic structured information, and the structured features have been proved effective in relation extraction. In this paper, we proposed another way to efficiently utilize structured features but in a weakly learning way. Co-training algorithm was chosen by us, the structured features were set to be one view of it, and the flat features were set to be the other. Through using co-training algorithm, we can combine both flat and structured information for relation extraction.
Keywords :
feature extraction; learning (artificial intelligence); text analysis; co-training algorithm; flat information; parse tree; relation extraction; syntactic structured information; Convolution; Data mining; Event detection; Feature extraction; Intelligent networks; Intelligent structures; International collaboration; Kernel; Laboratories; Support vector machines; cotraining; kernel method; relation extraction;
Conference_Titel :
Intelligent Networking and Collaborative Systems, 2009. INCOS '09. International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-5165-4
Electronic_ISBN :
978-0-7695-3858-7
DOI :
10.1109/INCOS.2009.15
