Generating Timelines by Modeling Semantic Change

TL;DR

This paper presents a method for creating timelines of semantic change by analyzing how words evolve in relation to historical events using static and dynamic word embeddings.

Contribution

It introduces a novel approach that combines static and time-varying embeddings to identify and quantify semantic changes and influential events over time.

Findings

Successfully captures semantic change in words over time

Effectively identifies key historical events influencing language

Provides both qualitative and quantitative validation

Abstract

Though languages can evolve slowly, they can also react strongly to dramatic world events. By studying the connection between words and events, it is possible to identify which events change our vocabulary and in what way. In this work, we tackle the task of creating timelines - records of historical "turning points", represented by either words or events, to understand the dynamics of a target word. Our approach identifies these points by leveraging both static and time-varying word embeddings to measure the influence of words and events. In addition to quantifying changes, we show how our technique can help isolate semantic changes. Our qualitative and quantitative evaluations show that we are able to capture this semantic change and event influence.