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Learning foci for question answering over topic maps

Paper, was published by Rani Pinchuk, Alexander Mikhailian, and Tiphaine Dalmas at 2009-08-04

This paper introduces the concepts of asking point and expected answer type as variations of the question focus.

External Links: download paper and ACM record


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.

Authors

Rani Pinchuk

No contact information available. 

Rani_2010_150x150

Rani is involved in LINDO, DIADEM, ULISSE, TopiEngi, and SATOPI.

Alexander Mikhailian

No contact information available. 

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Alexander is author of A case for XTM 3.0, XTM 1.0 to XTM 2.0.. , Automated Focus Extraction.. , and Learning foci for question.. .

Tiphaine Dalmas

No contact information available. 

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Tiphaine is author of Automated Focus Extraction.. and Learning foci for question.. .

 

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