Rule-based and Statistical Approaches to Morpho-syntactic Tagging of German

Rule-based and statistical approaches constitute the two leading paradigms in computational linguistics. This paper applies the two types of approaches to the task of assigning morpho-syntactic categories to words in German, a language with rich inectional morphology. The rule-based approach uses the Xerox Incremental Deep Parsing System and provides a novel constraint-based framework that integrates phrase-internal concord rules and phrase-external syntactic heuristics into one uniform architecture. The statistical approach utilizes the PCFG-parser LoPar which yields acceptable results even for moderate amounts of manuallyannotated treebank training data. It is shown that tree transformations constitute a crucial step in weakening the independence assumptions inherent in probabilistic context-free grammars and in optimizing the performance for the task at hand