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Support Vector Machine with Ensemble Tree Kernel for Relation Extraction
Joint Authors
Liu, Xiaoyong
Fu, Hui
Du, Zhiguo
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-03-22
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Xiaoyong& Fu, Hui& Du, Zhiguo. 2016. Support Vector Machine with Ensemble Tree Kernel for Relation Extraction. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1099805
Modern Language Association (MLA)
Liu, Xiaoyong…[et al.]. Support Vector Machine with Ensemble Tree Kernel for Relation Extraction. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1099805
American Medical Association (AMA)
Liu, Xiaoyong& Fu, Hui& Du, Zhiguo. Support Vector Machine with Ensemble Tree Kernel for Relation Extraction. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1099805
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099805