  We describe and experimentally evaluate a method for automatically clustering
words according to their distribution in particular syntactic contexts.
Deterministic annealing is used to find lowest distortion sets of clusters. As
the annealing parameter increases, existing clusters become unstable and
subdivide, yielding a hierarchical ``soft'' clustering of the data. Clusters
are used as the basis for class models of word coocurrence, and the models
evaluated with respect to held-out test data.
