  The problem of extracting important and meaningful parts of a sensory data
stream, without prior training, is studied for symbolic sequences, by using
textual narrative as a test case. This is part of a larger study concerning the
extraction of concepts from spacetime processes, and their knowledge
representations within hybrid symbolic-learning `Artificial Intelligence'. Most
approaches to text analysis make extensive use of the evolved human sense of
language and semantics. In this work, streams are parsed without knowledge of
semantics, using only measurable patterns (size and time) within the changing
stream of symbols -- as an event `landscape'. This is a form of interferometry.
Using lightweight procedures that can be run in just a few seconds on a single
CPU, this work studies the validity of the Semantic Spacetime Hypothesis, for
the extraction of concepts as process invariants. This `semantic preprocessor'
may then act as a front-end for more sophisticated long-term graph-based
learning techniques. The results suggest that what we consider important and
interesting about sensory experience is not solely based on higher reasoning,
but on simple spacetime process cues, and this may be how cognitive processing
is bootstrapped in the beginning.
