In this paper we present the results of the combination of stochastic and rule-based disambiguation methods applied to Basque languagel. The methods we have used in disambiguation are Constraint Grammar formalism and an HMM based tagger developed within the MULTEXT project. As Basque is an agglutinative language, a morphological analyser is needed to attach all possible readings to each word. Then, CG rules are applied using all the morphological features and this process decreases

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... ambiguity of texts. Finally, we use the MULTEXT project tools to select just one from the possible remaining tags. Using only the stochastic method the error rate is about 14%, but the accuracy may be increased by about 2% enriching the lexicon with the unknown words. When both methods are combined, the error rate of the whole process is 3.5%. Considering that the training corpus is quite small, that the HMM model is a first order one and that Constraint Grammar of Basque language is still in progress, we think that this combined method can achieve good results, and it would be appropriate for other agglutinative languages.