2010 SemanticRelationExtractionwithK

• (Reichartz et al., 2010) ⇒ Frank Reichartz, Hannes Korte, and Gerhard Paass. (2010). “Semantic Relation Extraction with Kernels over Typed Dependency Trees.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835902

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Cited By

• http://scholar.google.com/scholar?q=%22Semantic+relation+extraction+with+kernels+over+typed+dependency+trees%22+2010
• http://portal.acm.org/citation.cfm?id=1835902&preflayout=flat#citedby

Quotes

Author Keywords

algorithms; design; information extraction; natural language processing; relation extraction; text mining.

Abstract

An important step for understanding the semantic content of text is the extraction of semantic relations between entities in natural language documents. Automatic extraction techniques have to be able to identify different versions of the same relation which usually may be expressed in a great variety of ways. Therefore these techniques benefit from taking into account many syntactic and semantic features, especially parse trees generated by automatic sentence parsers. Typed dependency parse trees are edge and node labeled parse trees whose labels and topology contains valuable semantic clues. This information can be exploited for relation extraction by the use of kernels over structured data for classification. In this paper we present new tree kernels for relation extraction over typed dependency parse trees. On a public benchmark data set we are able to demonstrate a significant improvement in terms of relation extraction quality of our new kernels over other state-of-the-art kernels.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 SemanticRelationExtractionwithK	Frank Reichartz Hannes Korte Gerhard Paass			Semantic Relation Extraction with Kernels over Typed Dependency Trees		KDD-2010 Proceedings		10.1145/1835804.1835902		2010