  We propose a theoretical framework within which information on the vocabulary
of a given corpus can be inferred on the basis of statistical information
gathered on that corpus. Inferences can be made on the categories of the words
in the vocabulary, and on their syntactical properties within particular
languages. Based on the same statistical data, it is possible to build matrices
of syntagmatic similarity (bigram transition matrices) or paradigmatic
similarity (probability for any pair of words to share common contexts). When
clustered with respect to their syntagmatic similarity, words tend to group
into sublanguage vocabularies, and when clustered with respect to their
paradigmatic similarity, into syntactic or semantic classes. Experiments have
explored the first of these two possibilities. Their results are interpreted in
the frame of a Markov chain modelling of the corpus' generative processe(s): we
show that the results of a spectral analysis of the transition matrix can be
interpreted as probability distributions of words within clusters. This method
yields a soft clustering of the vocabulary into sublanguages which contribute
to the generation of heterogeneous corpora. As an application, we show how
multilingual texts can be visually segmented into linguistically homogeneous
segments. Our method is specifically useful in the case of related languages
which happened to be mixed in corpora.
