Paper: Log-Linear Models For Wide-Coverage CCG Parsing

ACL ID W03-1013
Title Log-Linear Models For Wide-Coverage CCG Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2003
Authors

This paper describes log-linear pars- ing models for Combinatory Categorial Grammar (CCG). Log-linear models can easily encode the long-range dependen- cies inherent in coordination and extrac- tion phenomena, which CCG was designed to handle. Log-linear models have pre- viously been applied to statistical pars- ing, under the assumption that all possible parses for a sentence can be enumerated. Enumerating all parses is infeasible for large grammars; however, dynamic pro- gramming over a packed chart can be used to efficiently estimate the model parame- ters. We describe a parellelised implemen- tation which runs on a Beowulf cluster and allows the complete WSJ Penn Treebank to be used for estimation.