Abstract
A model of a natural language text is a collection of information that approximates the statistics and structure of the text being modeled. The purpose of the model may be to give insight into rules which govern how language is generated, or to predict properties of future samples of it. This paper studies models of natural language from three different, but related, viewpoints. First, we examine the statistical regularities that are found empirically, based on the natural units of words and letters. Second, we study theoretical models of language, including simple random generative models of letters and words whose output, like genuine natural language, obeys Zipf's law. Innovation in text is also considered by modeling the appearance of previously unseen words as a Poisson process. Finally, we review experiments that estimate the information content inherent in natural text.
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