Support Vector Machine with Ensemble Tree Kernel for Relation Extraction.
Relation extraction is one of the important research topics in the field of information extraction research. To solve the problem of semantic variation in traditional semisupervised relation extraction algorithm, this paper proposes a novel semisupervised relation extraction algorithm based on ensemble learning (LXRE). The new algorithm mainly uses two kinds of support vector machine classifiers based on tree kernel for integration and integrates the strategy of constrained extension seed set. The new algorithm can weaken the inaccuracy of relation extraction, which is caused by the phenomenon of semantic variation. The numerical experimental research based on two benchmark data sets (PropBank and AIMed) shows that the LXRE algorithm proposed in the paper is superior to other two common relation extraction methods in four evaluation indexes (Precision, Recall, F-measure, and Accuracy). It indicates that the new algorithm has good relation extraction ability compared with others.
Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China.
Xiaoyong
0000-0002-0795-841X
X
Liu
Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China.
Hui
H
Fu
College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong 510642, China.
Zhiguo
Z
Du
D000465:Algorithms; D006801:Humans; D016247:Information Storage and Retrieval; D012660:Semantics; D060388:Support Vector Machine