In addition we are interested primarily in inducing small numbers of clusters (at most 128) from comparatively small amounts of data using limited or no sources of external knowledge, and in approaches that will work across a wide range of languages, rather than inducing large numbers (say 1000) from hundreds of millions of words.
secondly, because of the Zipfian distribution of word frequencies, simple baselines that assign each frequent word to a different class, can score rather highly, as we shall see below.