Learning foci for question answering over topic maps

Authors:
Alexander Mikhailian;Tiphaine Dalmas;Rani Pinchuk
Affiliations:
Space Application Services, Zaventem, Belgium;Aethys;Space Application Services, Zaventem, Belgium
Venue:
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Year:
2009

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Abstract

This paper introduces the concepts of asking point and expected answer type as variations of the question focus. They are of particular importance for QA over semistructured data, as represented by Topic Maps, OWL or custom XML formats. We describe an approach to the identification of the question focus from questions asked to a Question Answering system over Topic Maps by extracting the asking point and falling back to the expected answer type when necessary. We use known machine learning techniques for expected answer type extraction and we implement a novel approach to the asking point extraction. We also provide a mathematical model to predict the performance of the system.