Abstract
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 inflectional 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 manually-annotated 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.
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Hinrichs, E.W., Trushkina, J.S. (2004). Rule-based and Statistical Approaches to Morpho-syntactic Tagging of German. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 25. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39985-8_57
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DOI: https://doi.org/10.1007/978-3-540-39985-8_57
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