Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (I) : Bootstrapping Meaning from Episodic Narrative viewed as a Feature Landscape

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.