Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (II) : Establishing the Geometry of Invariant Concepts, Themes, and Namespaces

TL;DR

This paper explores an unsupervised, multiscale approach to analyze narrative texts by representing them as spacetime graphs, revealing invariant concepts and themes without linguistic prior knowledge.

Contribution

It introduces a bioinformatics-inspired, multiscale method for extracting invariant concepts and themes from natural language narratives using spacetime relationships.

Findings

Successful unsupervised analysis of narrative data streams

Identification of concepts and themes across multiple scales

Construction of narrative graphs encoding spacetime relationships

Abstract

Given a pool of observations selected from a sensor stream, input data can be robustly represented, via a multiscale process, in terms of invariant concepts, and themes. Applying this to episodic natural language data, one may obtain a graph geometry associated with the decomposition, which is a direct encoding of spacetime relationships for the events. This study contributes to an ongoing application of the Semantic Spacetime Hypothesis, and demonstrates the unsupervised analysis of narrative texts using inexpensive computational methods without knowledge of linguistics. Data streams are parsed and fractionated into small constituents, by multiscale interferometry, in the manner of bioinformatic analysis. Fragments may then be recombined to construct original sensory episodes---or form new narratives by a chemistry of association and pattern reconstruction, based only on the four fundamental spacetime relationships. There is a straightforward correspondence between bioinformatic processes and this cognitive representation of natural language. Features identifiable as `concepts' and `narrative themes' span three main scales (micro, meso, and macro). Fragments of the input act as symbols in a hierarchy of alphabets that define new effective languages at each scale.