2006 ACompositeKernelForRelExtr

(Zhang et al., 2006a) ⇒ Min Zhang, Jie Zhang, Jian Su, Guodong Zhou. (2006). “A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features.” In: Proceedings of COLING-ACL (ACL 2006). doi:10.3115/1220175.1220279

Subject Headings: Relation Recognition from Text Algorithm, ACE Benchmark Task

Notes

It defines a Syntactic Path-Enclosed Subtree.

Cited By

~72 http://scholar.google.com/scholar?cites=17464379114785860634

2008

(Sarawagi, 2008) ⇒ Sunita Sarawagi. (2008). “Information Extraction.” In: Foundations and Trends in Databases, 1(3). doi:10.1561/1900000003

Quotes

Abstract

This paper proposes a novel composite kernel for relation extraction. The composite kernel consists of two individual kernels: an entity kernel that allows for entity-related features and a convolution parse tree kernel that models syntactic information of relation examples. The motivation of our method is to fully utilize the nice properties of kernel methods to explore diverse knowledge for relation extraction. Our study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features. Evaluation on the ACE corpus shows that our method outperforms the previous best-reported methods and significantly outperforms previous two dependency tree kernels for relation extraction."

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 ACompositeKernelForRelExtr	Min Zhang Jian Su Jie Zhang GuoDong Zhou			A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features		Proceedings of COLING-ACL	http://acl.ldc.upenn.edu/P/P06/P06-1104.pdf	10.3115/1220175.1220279		2006