  Lexical ambiguity makes it difficult to compute various useful statistics of
a corpus. A given word form might represent any of several morphological
feature bundles. One can, however, use unsupervised learning (as in EM) to fit
a model that probabilistically disambiguates word forms. We present such an
approach, which employs a neural network to smoothly model a prior distribution
over feature bundles (even rare ones). Although this basic model does not
consider a token's context, that very property allows it to operate on a simple
list of unigram type counts, partitioning each count among different analyses
of that unigram. We discuss evaluation metrics for this novel task and report
results on 5 languages.
