# Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings

arXiv: https://arxiv.org/abs/2205.08256 · HTML: https://mlsanity.com/paper/2205.08256/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 10 of 10)

- [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)
- [Diachronic word embeddings and semantic shifts: a survey](https://mlsanity.com/paper/1806.03537/) (2018)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [Survey of Computational Approaches to Lexical Semantic Change](https://mlsanity.com/paper/1811.06278/) (2018)
- [Temporal Analysis of Language through Neural Language Models](https://mlsanity.com/paper/1405.3515/) (2014)
- [Dynamic Word Embeddings](https://mlsanity.com/paper/1702.08359/) (2017)
- [Dynamic Word Embeddings for Evolving Semantic Discovery](https://mlsanity.com/paper/1703.00607/) (2017)
- [On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models](https://mlsanity.com/paper/1704.02497/) (2017)
- [Deep Sound Change: Deep and Iterative Learning, Convolutional Neural Networks, and Language Change](https://mlsanity.com/paper/2011.05463/) (2020)

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

- [A Case Against Implicit Standards: Homophone Normalization in Machine Translation for Languages that use the Ge'ez Script](https://mlsanity.com/paper/2507.15142/) (2025)
- [Exploring Diachronic and Diatopic Changes in Dialect Continua: Tasks, Datasets and Challenges](https://mlsanity.com/paper/2407.04010/) (2024)
- [Exploring Sound Change Over Time: A Review of Computational and Human Perception](https://mlsanity.com/paper/2407.05092/) (2024)

## Abstract (arXiv)

While a great deal of work has been done on NLP approaches to lexical semantic change detection, other aspects of language change have received less attention from the NLP community. In this paper, we address the detection of sound change through historical spelling. We propose that a sound change can be captured by comparing the relative distance through time between their distributions using PPMI character embeddings. We verify this hypothesis in synthetic data and then test the method's ability to trace the well-known historical change of lenition of plosives in Danish historical sources. We show that the models are able to identify several of the changes under consideration and to uncover meaningful contexts in which they appeared. The methodology has the potential to contribute to the study of open questions such as the relative chronology of sound shifts and their geographical distribution.