SCoT: Sense Clustering over Time: a tool for the analysis of lexical change

TL;DR

SCoT is a hybrid network-based tool that visualizes and analyzes lexical change over time by creating and merging sense graphs from time-aggregated data, providing detailed insights into semantic evolution.

Contribution

It introduces a novel hybrid approach combining discrete and continuous network analysis for lexical change, with a new dynamic sense graph model.

Findings

Successfully applied to study the changing meaning of 'crisis' in European texts.

Provides detailed visualizations of sense formation, change, and disappearance.

Enhances understanding of lexical evolution over continuous time intervals.

Abstract

We present Sense Clustering over Time (SCoT), a novel network-based tool for analysing lexical change. SCoT represents the meanings of a word as clusters of similar words. It visualises their formation, change, and demise. There are two main approaches to the exploration of dynamic networks: the discrete one compares a series of clustered graphs from separate points in time. The continuous one analyses the changes of one dynamic network over a time-span. SCoT offers a new hybrid solution. First, it aggregates time-stamped documents into intervals and calculates one sense graph per discrete interval. Then, it merges the static graphs to a new type of dynamic semantic neighbourhood graph over time. The resulting sense clusters offer uniquely detailed insights into lexical change over continuous intervals with model transparency and provenance. SCoT has been successfully used in a European study on the changing meaning of `crisis'.