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When looking at the structure of natural language, "phrases" and "words" arecentral notions. We consider the problem of identifying such "meaningfulsubparts" of language of any length and underlying composition principles in acompletely corpus-based and language-independent way without using any kind ofprior linguistic knowledge. Unsupervised methods for identifying "phrases",mining subphrase structure and finding words in a fully automated way aredescribed. This can be considered as a step towards automatically computing a"general dictionary and grammar of the corpus". We hope that in the long runvariants of our approach turn out to be useful for other kind of sequence dataas well, such as, e.g., speech, genom sequences, or music annotation. Even ifwe are not primarily interested in immediate applications, results obtained fora variety of languages show that our methods are interesting for many practicaltasks in text mining, terminology extraction and lexicography, search enginetechnology, and related fields.

Corpus analysis without prior linguistic knowledge - unsupervised mining of phrases and subphrase structure