Weakly-supervised Bayesian learning of a CCG supertagger

Abstract

We present a Bayesian formulation for weakly-supervised learning of a Combinatory Categorial Grammar (CCG) supertagger with an HMM. We assume supervision in the form of a tag dictionary, and our prior encourages the use of cross-linguistically common category structures as well as transitions between tags that can combine locally according to CCG’s combinators. Our prior is theoretically appealing since it is motivated by language-independent, universal properties of the CCG formalism. Empirically, we show that it yields substantial improvements over previous work that used similar biases to initialize an EM-based learner. Additional gains are obtained by further shaping the prior with corpus-specific information that is extracted automatically from raw text and a tag dictionary.