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Composite kernels for relation extraction

 
: Reichartz, F.; Korte, H.; Paaß, G.

:
Preprint urn:nbn:de:0011-n-1051329 (172 KByte PDF)
MD5 Fingerprint: abae964de3003f8c6b3a11aa8ce2b3d9
Created on: 8.10.2009


Association for Computational Linguistics -ACL-; Asian Federation of Natural Language Processing -AFNLP-:
ACL-IJCNLP 2009. Proceedings : Joint conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing. 4 August 2009 Suntec, Singapore
Stroudsburg, Pa.: ACL, 2009
pp.365-368
Association for Computational Linguistics (Annual Meeting) <47, 2009, Singapore>
International Joint Conference on Natural Language Processing (IJCNLP) <4, 2009, Singapore>
English
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
relation extraction; tree kernels; machine learning

Abstract
The automatic extraction of relations between entities expressed in natural language text is an important problem for IR and text understanding. In this paper we show how different kernels for parse trees can be combined to improve the relation extraction quality. On a public benchmark dataset the combination of a kernel for phrase grammar parse trees and for dependency parse trees outperforms all known tree kernel approaches alone suggesting that both types of trees contain complementary information for relation extraction.

: http://publica.fraunhofer.de/documents/N-105132.html