Paper: Extracting Relations With Integrated Information Using Kernel Methods

ACL ID P05-1052
Title Extracting Relations With Integrated Information Using Kernel Methods
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

Entity relation detection is a form of in- formation extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from differ- ent levels of syntactic processing using kernel methods. Information from three different levels of processing is consid- ered: tokenization, sentence parsing and deep dependency analysis. Each source of information is represented by kernel func- tions. Then composite kernels are devel- oped to integrate and extend individual kernels so that processing errors occurring at one level can be overcome by informa- tion from other levels. We present an evaluation of these methods on the 2004 ACE relation detection task, using Sup- port Vector Machines, and show that each level of synt...