Modern natural language interfaces to databases: composing statistical parsing with semantic tractability

  • Authors:
  • Ana-Maria Popescu;Alex Armanasu;Oren Etzioni;David Ko;Alexander Yates

  • Affiliations:
  • University of Washington;University of Washington;University of Washington;University of Washington;University of Washington

  • Venue:
  • COLING '04 Proceedings of the 20th international conference on Computational Linguistics
  • Year:
  • 2004

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Abstract

Natural Language Interfaces to Databases (NLIs) can benefit from the advances in statistical parsing over the last fifteen years or so. However, statistical parsers require training on a massive, labeled corpus, and manually creating such a corpus for each database is prohibitively expensive. To address this quandary, this paper reports on the PRECISE NLI, which uses a statistical parser as a "plug in". The paper shows how a strong semantic model coupled with "light re-training" enables PRECISE to overcome parser errors, and correctly map from parsed questions to the corresponding SQL queries. We discuss the issues in using statistical parsers to build database-independent NLIs, and report on experimental results with the benchmark ATIS data set where PRECISE achieves 94% accuracy.