# Lexical semantic change for Ancient Greek and Latin

arXiv: https://arxiv.org/abs/2101.09069 · HTML: https://mlsanity.com/paper/2101.09069/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 88 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (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)
- [Diachronic word embeddings and semantic shifts: a survey](https://mlsanity.com/paper/1806.03537/) (2018)
- [Statistically Significant Detection of Linguistic Change](https://mlsanity.com/paper/1411.3315/) (2014)
- [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)
- [Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change](https://mlsanity.com/paper/1804.06517/) (2018)
- [Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change](https://mlsanity.com/paper/1906.01688/) (2019)
- [A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains](https://mlsanity.com/paper/1906.02979/) (2019)
- [Challenges for Computational Lexical Semantic Change](https://mlsanity.com/paper/2101.07668/) (2021)
- [That's sick dude!: Automatic identification of word sense change across different timescales](https://mlsanity.com/paper/1405.4392/) (2014)
- [On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models](https://mlsanity.com/paper/1704.02497/) (2017)
- [GASC: Genre-Aware Semantic Change for Ancient Greek](https://mlsanity.com/paper/1903.05587/) (2019)
- [Poisson Random Fields for Dynamic Feature Models](https://mlsanity.com/paper/1611.07460/) (2016)
- [German in Flux: Detecting Metaphoric Change via Word Entropy](https://mlsanity.com/paper/1706.04971/) (2017)
- [Modeling Language Change in Historical Corpora: The Case of Portuguese](https://mlsanity.com/paper/1610.00030/) (2016)
- [Customised Structural Elicitation](https://mlsanity.com/paper/1807.03693/) (2018)
- [GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering](https://mlsanity.com/paper/2005.09946/) (2020)

## Built on by (most cited first, 4 of 4)

- [DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages](https://mlsanity.com/paper/2104.08540/) (2021)
- [PhiloBERTA: A Transformer-Based Cross-Lingual Analysis of Greek and Latin Lexicons](https://mlsanity.com/paper/2503.05265/) (2025)
- [Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark](https://mlsanity.com/paper/2510.19585/) (2025)
- [Dynamics of Meaning: Towards the Evaluation of Diachronic Semantic Change in Sinhala](https://mlsanity.com/paper/2609.08609/) (2026)

## Abstract (arXiv)

Change and its precondition, variation, are inherent in languages. Over time, new words enter the lexicon, others become obsolete, and existing words acquire new senses. Associating a word's correct meaning in its historical context is a central challenge in diachronic research. Historical corpora of classical languages, such as Ancient Greek and Latin, typically come with rich metadata, and existing models are limited by their inability to exploit contextual information beyond the document timestamp. While embedding-based methods feature among the current state of the art systems, they are lacking in the interpretative power. In contrast, Bayesian models provide explicit and interpretable representations of semantic change phenomena. In this chapter we build on GASC, a recent computational approach to semantic change based on a dynamic Bayesian mixture model. In this model, the evolution of word senses over time is based not only on distributional information of lexical nature, but also on text genres. We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-of-the-art embedding-based models. On top of providing a full description of meaning change over time, we show that Bayesian mixture models are highly competitive approaches to detect binary semantic change in both Ancient Greek and Latin.