Paper: Convolution Kernels on Constituent Dependency and Sequential Structures for Relation Extraction

ACL ID D09-1143
Title Convolution Kernels on Constituent Dependency and Sequential Structures for Relation Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

This paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. Syntax is derived from constituent and dependency parse trees whereas se- mantics concerns to entity types and lex- ical sequences. We investigate the effec- tiveness of such representations in the au- tomated relation extraction from texts. We process the above data by means of Sup- port Vector Machines along with the syn- tactic tree, the partial tree and the word sequence kernels. Our study on the ACE 2004 corpus illustrates that the combina- tion of the above kernels achieves high ef- fectiveness and significantly improves the current state-of-the-art.