  Several methods have been proposed for processing a corpus to induce a tagset
for the sub-language represented by the corpus. This paper examines a
structured-tag word classification method introduced by McMahon (1994) and
discussed further by McMahon & Smith (1995) in cmp-lg/9503011 . Two major
variations, (1) non-random initial assignment of words to classes and (2)
moving multiple words in parallel, together provide robust non-random results
with a speed increase of 200% to 450%, at the cost of slightly lower quality
than McMahon's method's average quality. Two further variations, (3) retaining
information from less- frequent words and (4) avoiding reclustering closed
classes, are proposed for further study.
  Note: The speed increases quoted above are relative to my implementation of
my understanding of McMahon's algorithm; this takes time measured in hours and
days on a home PC. A revised version of the McMahon & Smith (1995) paper has
appeared (June 1996) in Computational Linguistics 22(2):217- 247; this refers
to a time of "several weeks" to cluster 569 words on a Sparc-IPC.
