Ultrametric Model of Mind, II: Application to Text Content Analysis

TL;DR

This paper explores how ultrametric topology can be used to analyze the inherent structure of text content, potentially revealing unconscious thought processes through measurable ultrametricity.

Contribution

It demonstrates the application of ultrametric topology to a large collection of texts, establishing a method to measure inherent ultrametricity in language data.

Findings

Ultrametricity can be measured in large text collections

Text content exhibits significant ultrametric structure

Ultrametricity may relate to unconscious thought processes

Abstract

In a companion paper, Murtagh (2012), we discussed how Matte Blanco's work linked the unrepressed unconscious (in the human) to symmetric logic and thought processes. We showed how ultrametric topology provides a most useful representational and computational framework for this. Now we look at the extent to which we can find ultrametricity in text. We use coherent and meaningful collections of nearly 1000 texts to show how we can measure inherent ultrametricity. On the basis of our findings we hypothesize that inherent ultrametricty is a basis for further exploring unconscious thought processes.