  This paper describes the results of some experiments exploring statistical
methods to infer syntactic behavior of words and morphemes from a raw corpus in
an unsupervised fashion. It shares certain points in common with Brown et al
(1992) and work that has grown out of that: it employs statistical techniques
to analyze syntactic behavior based on what words occur adjacent to a given
word. However, we use an eigenvector decomposition of a nearest-neighbor graph
to produce a two-dimensional rendering of the words of a corpus in which words
of the same syntactic category tend to form neighborhoods. We exploit this
technique for extending the value of automatic learning of morphology. In
particular, we look at the suffixes derived from a corpus by unsupervised
learning of morphology, and we ask which of these suffixes have a consistent
syntactic function (e.g., in English, -tion is primarily a mark of nouns, but
-s marks both noun plurals and 3rd person present on verbs), and we determine
that this method works well for this task.
