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Relation Classification for Semantic Structure Annotation of Text

TitleRelation Classification for Semantic Structure Annotation of Text
Publication TypeConference Paper
Year of Publication2008
AuthorsYan Y, Matsuo Y, Ishizuka M, Yokoi T
Conference NameIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
PublisherIEEE Computer Society
Conference LocationToronto, Canada
ISBN Number978-0-7695-3496-1

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 ( 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 relation extraction. Preliminary evaluation on a manual dataset, using Support Vector Machine, shows that relations can be classified with good performance.