An unsupervised learning method, based on corpus linguistics and special language terminology, is described that can extract time-varying information from text streams. The method is shown to be 'language-independent' in that its use leads to sets of regular-expressions that can be used to extract the information in typologically distinct languages like English and Arabic. The method uses the information related to the distribution of Ngrams, for automatically extracting 'meaning bearing' patterns of usage in a training corpus. The analysis of an English news wire corpus (1,720,142 tokens) and Arabic news wire corpus (1,720,154 tokens) show encouraging results.