Paper: Learning foci for Question Answering over Topic Maps

ACL ID P09-2082
Title Learning foci for Question Answering over Topic Maps
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2009
Authors

This paper introduces the concepts of ask- ingpoint and expectedanswertype as vari- ations of the question focus. They are of particular importance for QA over semi- structured data, as represented by Topic Maps, OWL or custom XML formats. We describe an approach to the identifica- tion 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 an- swer type when necessary. We use known machine learning techniques for expected answer type extraction and we implement a novel approach to the asking point ex- traction. We also provide a mathematical model to predict the performance of the system.