Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (II) : Establishing the Geometry of Invariant Concepts, Themes, and Namespaces
This work addresses the challenge of narrative comprehension for AI systems by applying the Semantic Spacetime Hypothesis, but it appears incremental as it builds on prior applications without demonstrating broad advancements.
The study tackles the problem of representing episodic natural language data in terms of invariant concepts and themes using unsupervised, inexpensive computational methods, resulting in a graph geometry that encodes spacetime relationships for events without relying on linguistic knowledge.
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.