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

Published: 23 August 2004 Publication History

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.

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

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      cover image DL Hosted proceedings
      COLING '04: Proceedings of the 20th international conference on Computational Linguistics
      August 2004
      1411 pages

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      Association for Computational Linguistics

      United States

      Publication History

      Published: 23 August 2004

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      COLING '04 Paper Acceptance Rate 1,411 of 1,411 submissions, 100%;
      Overall Acceptance Rate 1,537 of 1,537 submissions, 100%

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      • (2024)SQLucid: Grounding Natural Language Database Queries with Interactive ExplanationsProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676368(1-20)Online publication date: 13-Oct-2024
      • (2024)Insights into Natural Language Database Query Errors: from Attention Misalignment to User Handling StrategiesACM Transactions on Interactive Intelligent Systems10.1145/365011414:4(1-32)Online publication date: 2-Mar-2024
      • (2023)Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised ApproachProceedings of the ACM on Management of Data10.1145/36267561:4(1-27)Online publication date: 12-Dec-2023
      • (2023)An Empirical Study of Model Errors and User Error Discovery and Repair Strategies in Natural Language Database QueriesProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584067(633-649)Online publication date: 27-Mar-2023
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