Analyzing Polysemy Evolution Using Semantic Cells

TL;DR

This paper presents a novel evolutionary framework for analyzing how word polysemy develops over time through Semantic Cells, challenging traditional static learning paradigms.

Contribution

It introduces a method to study the evolution of polysemy by modifying Semantic Cells and analyzing sentence sequences, providing a new perspective on semantic change.

Findings

Polysemy increases monotonically when senses are ordered by their evolution.

Semantic Cells can model the dynamic evolution of word senses.

The approach offers a new methodology for semantic analysis from an evolutionary perspective.

Abstract

The senses of words evolve. The sense of the same word may change from today to tomorrow, and multiple senses of the same word may be the result of the evolution of each other, that is, they may be parents and children. If we view Juba as an evolving ecosystem, the paradigm of learning the correct answer, which does not move with the sense of a word, is no longer valid. This paper is a case study that shows that word polysemy is an evolutionary consequence of the modification of Semantic Cells, which has al-ready been presented by the author, by introducing a small amount of diversity in its initial state as an example of analyzing the current set of short sentences. In particular, the analysis of a sentence sequence of 1000 sentences in some order for each of the four senses of the word Spring, collected using Chat GPT, shows that the word acquires the most polysemy monotonically in the analysis when the senses are arranged in the order in which they have evolved. In other words, we present a method for analyzing the dynamism of a word's acquiring polysemy with evolution and, at the same time, a methodology for viewing polysemy from an evolutionary framework rather than a learning-based one.