AbstractA reductionistic statistical framework for part-of-speech tagging and surface syntactic parsing is presented that has the same expressive power as the highly successful Constraint Grammar approach, see [Karlsson et al. 1995]. The structure of the Constraint Grammar rules allows them to be viewed as conditional probabilities that can be used to update the lexical tag probabilities, after which low-probability tags are repeatedly removed. Experiments using strictly conventional information sources on the Susanne and Teleman corpora indicate that the system performs as well as a traditional HMM-based part-of-speech tagger, yielding state-of-the-art results. The scheme also enables using the same information sources as the Constraint Grammar approach, and the hope is that it can improve on the performance of both statistical taggers and surface-syntactic analyzers.