The Correspondence Analysis Platform for Uncovering Deep Structure in
  Data and Information
  We study two aspects of information semantics: (i) the collection of all
relationships, (ii) tracking and spotting anomaly and change. The first is
implemented by endowing all relevant information spaces with a Euclidean metric
in a common projected space. The second is modelled by an induced ultrametric.
A very general way to achieve a Euclidean embedding of different information
spaces based on cross-tabulation counts (and from other input data formats) is
provided by Correspondence Analysis. From there, the induced ultrametric that
we are particularly interested in takes a sequential - e.g. temporal - ordering
of the data into account. We employ such a perspective to look at narrative,
"the flow of thought and the flow of language" (Chafe). In application to
policy decision making, we show how we can focus analysis in a small number of
dimensions.
