This paper describes the grling-sdm system, which is asupervised probabilistic classifier that participated in the 1998SENSEVAL competition for word-sense disambiguation. This systemuses model search to select decomposable probability models describingthe dependencies among the feature variables.These types of models have been found to be advantageous in terms ofefficiency and representational power. Performance on the SENSEVALevaluation data is discussed.