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Relation Extraction based on Extended Composite Kernel using Flat Lexical Features  

Chai, Sung-Pil (한국과학기술정보연구원 정보기술연구실)
Jeong, Chang-Hoo (한국과학기술정보연구원 정보기술연구실)
Chai, Yun-Soo (한국과학기술정보연구원 정보기술연구실)
Myaeng, Sung-Hyon (한국과학기술원 전산학과)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.8, 2009 , pp. 642-652 More about this Journal
Abstract
In order to improve the performance of the existing relation extraction approaches, we propose a method for combining two pivotal concepts which play an important role in classifying semantic relationships between entities in text. Having built a composite kernel-based relation extraction system, which incorporates both entity features and syntactic structured information of relation instances, we define nine classes of lexical features and synthetically apply them to the system. Evaluation on the ACE RDC corpus shows that our approach boosts the effectiveness of the existing composite kernels in relation extraction. It also confirms that by integrating the three important features (entity features, syntactic structures and contextual lexical features), we can improve the performance of a relation extraction process.
Keywords
Information Extraction; Relation Extraction; Machine Learning; Support Vector Machines; Composite Kernels; Convolution Parse Tree Kernels; Flat Lexical Features;
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