# Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport

arXiv: https://arxiv.org/abs/2412.12569 · HTML: https://mlsanity.com/paper/2412.12569/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://mlsanity.com/paper/1808.09121/) (2018)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Analysing Lexical Semantic Change with Contextualised Word Representations](https://mlsanity.com/paper/2004.14118/) (2020)
- [Statistically Significant Detection of Linguistic Change](https://mlsanity.com/paper/1411.3315/) (2014)
- [Dynamic Word Embeddings for Evolving Semantic Discovery](https://mlsanity.com/paper/1703.00607/) (2017)
- [Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change](https://mlsanity.com/paper/1804.06517/) (2018)
- [Word Rotator's Distance](https://mlsanity.com/paper/2004.15003/) (2020)
- [UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection](https://mlsanity.com/paper/2005.00050/) (2020)
- [Unbalanced Optimal Transport through Non-negative Penalized Linear Regression](https://mlsanity.com/paper/2106.04145/) (2021)
- [DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages](https://mlsanity.com/paper/2104.08540/) (2021)
- [A Survey on Contextualised Semantic Shift Detection](https://mlsanity.com/paper/2304.01666/) (2023)
- [A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change](https://mlsanity.com/paper/2402.12011/) (2024)
- [A Relaxed Matching Procedure for Unsupervised BLI](https://mlsanity.com/paper/2010.07095/) (2020)
- [Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings](https://mlsanity.com/paper/2305.08654/) (2023)

## Built on by (most cited first, 3 of 3)

- [ChronoLens: Measuring Language Change Across Time, Languages, and Linguistic Levels](https://mlsanity.com/paper/2608.03507/) (2026)
- [Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change](https://mlsanity.com/paper/2609.30974/) (2026)
- [Contextual trajectory and incremental contextual displacement: Towards using LLMs to understand dynamic, utterance-specific meaning construction](https://mlsanity.com/paper/2610.00840/) (2026)

## Abstract (arXiv)

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.