Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport

TL;DR

This paper introduces a novel approach using Unbalanced Optimal Transport to measure semantic change at the level of individual word usages, providing more detailed insights into lexical semantic shifts over time.

Contribution

It proposes Sense Usage Shift (SUS), a new metric leveraging UOT to quantify usage-level semantic change, addressing limitations of existing embedding-based methods.

Findings

SUS effectively captures sense usage shifts over time.

The method unifies instance-level and word-level semantic change detection.

Results demonstrate improved sensitivity to semantic broadening and narrowing.

Abstract

Lexical semantic change detection aims to identify shifts in word meanings over time. While existing methods using embeddings from a diachronic corpus pair estimate the degree of change for target words, they offer limited insight into changes at the level of individual usage instances. To address this, we apply Unbalanced Optimal Transport (UOT) to sets of contextualized word embeddings, capturing semantic change through the excess and deficit in the alignment between usage instances. In particular, we propose Sense Usage Shift (SUS), a measure that quantifies changes in the usage frequency of a word sense at each usage instance. By leveraging SUS, we demonstrate that several challenges in semantic change detection can be addressed in a unified manner, including quantifying instance-level semantic change and word-level tasks such as measuring the magnitude of semantic change and the broadening or narrowing of meaning.