Detecting and assessing contextual change in diachronic text documents using context volatility

TL;DR

This paper introduces a novel measure called context volatility to quantify how much terms change their context over time in diachronic texts, capturing semantic dynamics beyond traditional change detection.

Contribution

It proposes a new, efficient method to compute context volatility, addressing computational challenges and providing a tool for analyzing semantic shifts in large text corpora.

Findings

Effective detection of contextual changes in synthetic data

Application to British newspaper texts demonstrates real-world utility

Method outperforms baseline in capturing semantic dynamics

Abstract

Terms in diachronic text corpora may exhibit a high degree of semantic dynamics that is only partially captured by the common notion of semantic change. The new measure of context volatility that we propose models the degree by which terms change context in a text collection over time. The computation of context volatility for a word relies on the significance-values of its co-occurrent terms and the corresponding co-occurrence ranks in sequential time spans. We define a baseline and present an efficient computational approach in order to overcome problems related to computational issues in the data structure. Results are evaluated both, on synthetic documents that are used to simulate contextual changes, and a real example based on British newspaper texts.