ML-Optimization of Ported Constraint Grammars
Eckhard Bick · 2014
In this paper, we describe how a Constraint Grammar with linguist-written rules can be optimized and ported to another language using a Machine Learning technique.The effects of rule movements, sorting, grammar-sectioning and systematic rule modifications are discussed and quantitatively evaluated.Statistical information is used to provide a baseline and to enhance the core of manual rules.The best-performing parameter combinations achieved part-of-speech F-scores of over 92 for a grammar ported from English to Danish, a considerable advance over both the statistical baseline (85.7), and the raw ported grammar (86.1).When the same technique was applied to an existing native Danish CG, error reduction was 10% (F=96.94).