SUMMARY : Session P17-E


Title Turning a Dependency Treebank into a PSG-style Constituent Treebank
Authors E. Bick
Abstract In this paper, we present and evaluate a new method to convert Constraint Grammar (CG) parses of running text into Constituent Treebanks. The conversion is two-step - first a grammar-based method is used to bridge the gap between raw CG annotation and full dependency structure, then phrase structure bracketing and non-terminal nodes are introduced by clustering sister dependents, effectively building one syntactic treebank on top of another. The method is compared with another approach (Bick 2003-2), where constituent structures are arrived at by employing a function-tag based Phrase Structure Grammar (PSG). Results are evaluated on a small reference corpus for both raw and revised CG input, with bracketing F-Scores of 87.5% for raw text and 97.1% for revised CG input, and a raw text edge label accuracy of 95.9% for forms and 86% for functions, or 99.7% and 99.4%, respectively, for revised CG. By applying the tools to the CG-only part of the Danish Arboretum treebank we were able to increase the size of the treebank by 86%, from 197.400 to 367.500 words.
Keywords NLP, Treebanks, Dependency Grammar, Constraint Grammar
Full paper Turning a Dependency Treebank into a PSG-style Constituent Treebank