PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dependency Tree Kernels for Relation Extraction from Natural Language Text
Frank Reichartz, Hannes Korte and Gerhard Paaß
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009 pp. 270-285, 2009.


The automatic extraction of relations from unstructured natural text is challenging but offers practical solutions for many problems like automatic text understanding and semantic retrieval. Relation extraction can be formulated as a classification problem using support vector machines and kernels for structured data that may include parse trees to account for syntactic structure. In this paper we present new tree kernels over dependency parse trees automatically generated from natural language text. Experiments on a public benchmark data set show that our kernels with richer structural features significantly outperform all published approaches for kernel-based relation extraction from dependency trees. In addition we optimize kernel computations to improve the actual runtime compared to previous solutions.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:6766
Deposited By:Frank Reichartz
Deposited On:08 March 2010