Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars

This paper addresses the problem of mapping natural language sentences to lambda–calculus encodings of their meaning. We describe a learning algorithm that takes as input a training set of sentences labeled with expressions in the lambda calculus. The algorithm induces a grammar for the problem, along with a log-linear model that represents a distribution over syntactic and semantic analyses conditioned on the input sentence. We apply the method to the task of learning natural language interfaces to databases and show that the learned parsers outperform previous methods in two benchmark database domains.

• Publication year

  2005

• Venue

  Conference on Uncertainty in Artificial Intelligence

• Publication date

  2005-07-26

• Fields of study

  Linguistics, Computer Science

• Identifiers
  arXiv 1207.1420
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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