A hybrid system is described which combines the strength of manual rulewriting and statistical learning, obtaining results superior to both methods if applied separately. The combination of a rule-based system and a statistical one is not parallel but serial: the rule-based system performing partial disambiguation with recall close to 100% is applied first, and a trigram HMM tagger runs on its results. An experiment in Czech tagging has been performed with encouraging results.

This paper presents a simple yet in practice very efficient technique serving for automatic detection of those positions in a partof-speech tagged corpus where an error is to be suspected. The approach is based on the idea of learning and later application of "negative bigrams", i.e. on the search for pairs of adjacent tags which constitute an incorrect configuration in a text of a particular language (in English, e.g., the bigram ARTICLE -FINITE VERB). Further, the paper describes the generalization of the "negative bigrams" into "negative n-grams", for any natural n, which indeed provides a powerful tool for error detection in a corpus. The implementation is also discussed, as well as evaluation of results of the approach when used for error detection in the NEGRA® corpus of German, and the general implications for the quality of results of statistical taggers. Illustrative examples in the text are taken from German, and hence at least a basic command of this language would be helpful for their understanding -due to the complexity of the necessary accompanying explanation, the examples are neither glossed nor translated. However, the central ideas of the paper should be understandable also without any knowledge of German.

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