Skip to content

Dependency tree kernels for relation extraction from natural language text

by Frank Reichartz, Hannes Korte, G. Paass
Machine Learning and Knowledge Discovery in Databases ()

Abstract

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.

Cite this document (BETA)

Readership Statistics

3 Readers on Mendeley
by Discipline
 
67% Linguistics
 
33% Computer Science
by Academic Status
 
67% Student > Ph. D. Student
 
33% Student > Postgraduate
by Country
 
33% Russia
 
33% Germany
 
33% Portugal

Sign up today - FREE

Mendeley saves you time finding and organizing research. Learn more

  • All your research in one place
  • Add and import papers easily
  • Access it anywhere, anytime

Start using Mendeley in seconds!

Sign up & Download

Already have an account? Sign in