Question answering performance on table data

Authors:
Xing Wei;Bruce Croft;David Pinto
Affiliations:
University of Massachusetts Amherst, Amherst, MA;University of Massachusetts Amherst, Amherst, MA;University of Massachusetts Amherst, Amherst, MA
Venue:
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
Year:
2004

Citing 2
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Abstract

Question answering (QA) on table data is a challenging information retrieval task. This paper describes a QA system for tables created with both machine learning and heuristic table extraction methods. Errors were analyzed in order to improve the system using government statistical data. We also apply these improvements on another type of table data set and show the experimental results.