Paper: Inducing Probabilistic CCG Grammars from Logical Form with Higher-Order Unification

ACL ID D10-1119
Title Inducing Probabilistic CCG Grammars from Logical Form with Higher-Order Unification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

This paper addresses the problem of learn- ing to map sentences to logical form, given training data consisting of natural language sentences paired with logical representations of their meaning. Previous approaches have been designed for particular natural languages or specific meaning representations; here we present a more general method. The approach induces a probabilistic CCG grammar that represents the meaning of individual words and defines how these meanings can be com- bined to analyze complete sentences. We use higher-order unification to define a hy- pothesis space containing all grammars con- sistent with the training data, and develop an online learning algorithm that efficiently searches this space while simultaneously es- timating the parameters of a log-linear parsing mode...