Abstract
Researches based on statistical information have been more significant in the field of natural language processing. The use of raw corpora is fascinating, as it is easy to obtain a certain amount of non-tagged texts. However raw corpora often contain unknown words and phrases, and this causes low accuracy of the experiments. Colloquialism has not been worked enough because of this problem, though the processing of colloquialism is strongly required for the emails and other tasks. In this paper we propose a simple method to obtain domain-specific sequences from unrestricted texts using statistical information only. Our method needs a non-tagged training corpus. We use the statistical information drawn from the training corpus to extract semantic character sequences automatically. We had experiments on sequence extraction on email texts, and succeeded in extracting significant semantic sequences in the test corpus. The sequences our system salvaged contain casual terms, proper nouns, and sequences with representation change such as pronunciation extension.
