Paper: Parsing The WSJ Using CCG And Log-Linear Models

ACL ID P04-1014
Title Parsing The WSJ Using CCG And Log-Linear Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

This paper describes and evaluates log-linear parsing models for Combinatory Categorial Grammar (CCG). A parallel implementation of the L-BFGS optimisation algorithm is described, which runs on a Beowulf cluster allowing the complete Penn Treebank to be used for estima- tion. We also develop a new efficient parsing algorithm for CCG which maximises expected recall of dependencies. We compare models which use all CCG derivations, including non- standard derivations, with normal-form models. The performances of the two models are com- parable and the results are competitive with ex- isting wide-coverage CCG parsers.