Modeling the dynamics of domain specific terminology in diachronic corpora

TL;DR

This paper introduces context volatility, a new measure for detecting semantic change in terms within diachronic corpora, demonstrated through a political science case study to identify periods of debate and semantic shifts.

Contribution

It proposes the novel concept of context volatility to quantify term dynamics and applies it to real-world data for key term extraction.

Findings

Context volatility effectively detects semantic change periods.

The measure identifies controversial debates and semantic transformations.

Application to political data demonstrates practical utility.

Abstract

In terminology work, natural language processing, and digital humanities, several studies address the analysis of variations in context and meaning of terms in order to detect semantic change and the evolution of terms. We distinguish three different approaches to describe contextual variations: methods based on the analysis of patterns and linguistic clues, methods exploring the latent semantic space of single words, and methods for the analysis of topic membership. The paper presents the notion of context volatility as a new measure for detecting semantic change and applies it to key term extraction in a political science case study. The measure quantifies the dynamics of a term's contextual variation within a diachronic corpus to identify periods of time that are characterised by intense controversial debates or substantial semantic transformations.