Relation Classification for Semantic Structure Annotation of Text

Abstract

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.