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Mophological processing, syntactic parsing and other useful tools have been proposed in the field of natural language processing (NLP). Many of those NLP tools take dictionary-based approaches. Thus these tools are often not very efficient with texts written in casual wordings or texts which contain many domain-specific terms, because of the lack of vocabulary.In this paper we propose a simple method to obtain domain-specific sequences from unrestricted texts using statistical information only. This method is language-independent.We had experiments on sequence extraction on email texts in Japanese, and succeeded in extracting significant semantic sequences in the test corpus. We tried morphological parsing on the test corpus with ChaSen, a Japanese dictionary-based morphological parser, and examined our system's efficiency in extraction of semantic sequences which were not recognized with ChaSen. Our system detected 69.06% of the unknown words correctly.

Automatic semantic sequence extraction from unrestricted non-tagged texts