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 …

One of the classical goals of research in artificial intelligence is to construct systems that
automatically recover the meaning of natural language text. Machine learning methods hold
significant potential for addressing many of the challenges involved with these systems. This
thesis presents new techniques for learning to map sentences to logical form-lambda-
calculus representations of their meanings. We first describe an approach to the context-
independent learning problem, where sentences are analyzed in isolation. We describe a …