  When looking at the structure of natural language, "phrases" and "words" are
central notions. We consider the problem of identifying such "meaningful
subparts" of language of any length and underlying composition principles in a
completely corpus-based and language-independent way without using any kind of
prior linguistic knowledge. Unsupervised methods for identifying "phrases",
mining subphrase structure and finding words in a fully automated way are
described. This can be considered as a step towards automatically computing a
"general dictionary and grammar of the corpus". We hope that in the long run
variants of our approach turn out to be useful for other kind of sequence data
as well, such as, e.g., speech, genom sequences, or music annotation. Even if
we are not primarily interested in immediate applications, results obtained for
a variety of languages show that our methods are interesting for many practical
tasks in text mining, terminology extraction and lexicography, search engine
technology, and related fields.
