Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
Luke Zettlemoyer, Michael Collins
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.
PDF Link: /papers/05/p658-zettlemoyer.pdf
AUTHOR = "Luke Zettlemoyer
and Michael Collins",
TITLE = "Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "658--666"