›› 2011, Vol. 26 ›› Issue (1): 45-56.doi: 10.1007/s11390-011-1110-2

• Special Section on Natural Language Processing • Previous Articles     Next Articles

Kernel-Based Semantic Relation Detection and Classification via Enriched Parse Tree Structure

Guo-Dong Zhou (周国栋), Senior Member, CCF, Member, ACM, IEEE and Qiao-Ming Zhu (朱巧明), Senior Member, CCF   

  1. NLP Lab, School of Computer Science and Technology, Soochow University, Suzhou 215006, China
  • Received:2009-12-28 Revised:2010-10-28 Online:2011-01-01 Published:2011-01-01
  • About author:Guo-Dong Zhou received the Ph.D. degree from the National University of Singapore in 1999. He joined the Institute for Infocomm Research, Singapore, in 1999, and had been associate scientist, scientist and associate lead scientist at the institute until August 2006. Currently, he is a professor at the School of Computer Science and Technology, Soochow University, Suzhou, China. His research interests include natural language processing, information extraction and machine learning. He is a senior member of CCF and has been the member of ACM and IEEE since 1999. Currently, he serves as an editorial board member of Computational Linguistics and an associate editor of ACM Transaction on Asian Language Information Processing.
    Qiao-Ming Zhu received his Ph.D. degree from Soochow University, Suzhou, China, in 2008. Currently, he is a professor at the university and acts as the deputy director of Department of Science, Technology and Industry. His research interests include natural language processing, information extraction and embedded systems. He is a senior member of CCF.
  • Supported by:

    Supported by the National Natural Science Foundation of China under Grant Nos. 60873150, 60970056 and 90920004.

This paper proposes a tree kernel method of semantic relation detection and classification (RDC) between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree kernel is presented to better capture the inherent structural information in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness and approximate matching of sub-trees. Second, an enriched parse tree structure is proposed to well derive necessary structural information, e.g., proper latent annotations, from a parse tree. Evaluation on the ACE RDC corpora shows that both the new tree kernel and the enriched parse tree structure contribute significantly to RDC and our tree kernel method much outperforms the state-of-the-art ones.

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