Paper: Weakly-Supervised Bayesian Learning of a CCG Supertagger

ACL ID W14-1615
Title Weakly-Supervised Bayesian Learning of a CCG Supertagger
Venue International Conference on Computational Natural Language Learning
Year 2014

We present a Bayesian formulation for weakly-supervised learning of a Combina- tory Categorial Grammar (CCG) supertag- ger with an HMM. We assume supervi- sion 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 ap- pealing 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 bi- ases to initialize an EM-based learner. Ad- ditional gains are obtained by further shap- ing the prior with corpus-specific informa- tion that is extracted automatically from raw text and a tag dic...