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# Looking for meaning in all the wrong places \\ (at the character level)

The ubiquity of data obeying Zipf's Law has made it a lightning rod,
attracting a number of ``explanations.'' More, these explanations come
from an extremely impressive set of original thinkers, from widely
ranging disciplines: \item Noam Chomsky, the most influential linguist
of the last 30 years; \item George Miller, the mathematical psychologist
famous for such insight as the ``$7\pm2$ chunks'' of memory limitation;
\item Herbert Simon, the Nobel Prize winning economist and one of the
fathers of artificial intelligence; \item Benoit Mandelbrot, the
computational physicist most famous for his work on fractals.

Herbert
Simon, a keen observer of much cognitive activity, suggests the ubiquity
of Zipf's Law across heterogeneous collections should make us somewhat
suspicious of its ability to address the ``fine structure'' of
linguistics: No one supposes that there is any connection between horse
kicks suffered by soldiers in the German army and blood clots on a
microscope slide other than that the same urn scheme provides a
satisfactory abstract model of both phenomena. It is in the same
direction that we shall look for an explanation of the observed close
similarities among the five classes of distributions listed above. [Simon55] \eq (With ``urn'' Simon is
referring to mathematical models, e.g., related to Poisson
processes.\dhfoot{Poisson processes} See §3.2.1.3 for more on the ``five
classes'' of Simon's models.)

We therefore begin this section by
reviewing a number of early attempts to explain the phenomena underlying
ZIpf's Law; its mathematical derivation is reserved for Chapter 5.

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