|Title||Relation Classification for Semantic Structure Annotation of Text|
|Publication Type||Conference Paper|
|Year of Publication||2008|
|Authors||Yan, Y, Matsuo, Y, Ishizuka, M, Yokoi, T|
|Conference Name||IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology|
|Publisher||IEEE Computer Society|
|Conference Location||Toronto, Canada|
Confronting the challenges of annotating naturally occurring text into a semantically structured form to facilitate automatic information extraction, current Semantic Role Labeling (SRL) systems have been specifically examining a semantic predicate-argument structure. Based on the Concept Description Language for Natural Language (CDL.nl) which is intended to describe the concept structure of text using a set of pre-defined semantic relations, we develop a parser to add a new layer of semantic annotation of natural language sentences as an extension of SRL. With the assumption that all relation instances are detected, we present a relation classification approach facing the challenges of CDL.nl relation extraction. Preliminary evaluation on a manual dataset, using Support Vector Machine, shows that CDL.nl relations can be classified with good performance.