Dependency Tree Kernels for Relation Extraction from Natural Language Text
published: Oct. 20, 2009, recorded: September 2009, views: 410
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Download slides: ecmlpkdd09_reichartz_dtkrenlt_01.pdf (746.8 KB)
Download slides: ecmlpkdd09_reichartz_dtkrenlt_01.ppt (589.5 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !