PRECISE on ATIS: semantic tractability and experimental results

Authors:
Ana-Maria Popescu;Alex Armanasu;Oren Etzioni;David Ko;Alexander Yates
Affiliations:
University of Washington, Department of Computer Science and Engineering, Seattle, Washington;University of Washington, Department of Computer Science and Engineering, Seattle, Washington;University of Washington, Department of Computer Science and Engineering, Seattle, Washington;University of Washington, Department of Computer Science and Engineering, Seattle, Washington;University of Washington, Department of Computer Science and Engineering, Seattle, Washington
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

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Abstract

The need for Natural Language Interfaces to databases (NLIs) has become increasingly acute as more and more people access information through their web browsers, PDAs, and cell phones. Yet NLIs are only usable if they map natural language questions to SQL queries correctly -- people are unwilling to trade reliable and predictable user interfaces for intelligent but unreliable ones. We describe a reliable NLI, PRECISE, that incorporates a modern statistical paser and a semantic module. PRECISE provably handles a large class of natural language questions correctly. On the benchmark ATIS data set, PRECISE achieves 93.8% accuracy.