ML-Optimization of Ported Constraint Grammars

Abstract

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).

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