# GASC: Genre-Aware Semantic Change for Ancient Greek

arXiv: https://arxiv.org/abs/1903.05587 · HTML: https://mlsanity.com/paper/1903.05587/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2019.

## Built on by (most cited first, 10 of 10)

- [DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages](https://mlsanity.com/paper/2104.08540/) (2021)
- [Challenges for Computational Lexical Semantic Change](https://mlsanity.com/paper/2101.07668/) (2021)
- [Lexical Semantic Change Discovery](https://mlsanity.com/paper/2106.03111/) (2021)
- [NLP for The Greek Language: A Longer Survey](https://mlsanity.com/paper/2408.10962/) (2024)
- [Lexical semantic change for Ancient Greek and Latin](https://mlsanity.com/paper/2101.09069/) (2021)
- [Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018](https://mlsanity.com/paper/2105.00846/) (2021)
- [Measuring diachronic sense change: new models and Monte Carlo methods for Bayesian inference](https://mlsanity.com/paper/2105.00819/) (2021)
- [DHPLT: large-scale multilingual diachronic corpora and word representations for semantic change modelling](https://mlsanity.com/paper/2602.11968/) (2026)
- [An Embedded Diachronic Sense Change Model with a Case Study from Ancient Greek](https://mlsanity.com/paper/2311.00541/) (2023)
- [A Computational Approach to Measuring Semantic Change in Sanskrit Literature](https://mlsanity.com/paper/2609.25012/) (2026)

## Abstract (arXiv)

Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word's correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian models for semantic change have emerged as a powerful tool to address this challenge, providing explicit and interpretable representations of semantic change phenomena. However, while corpora typically come with rich metadata, existing models are limited by their inability to exploit contextual information (such as text genre) beyond the document time-stamp. This is particularly critical in the case of ancient languages, where lack of data and long diachronic span make it harder to draw a clear distinction between polysemy (the fact that a word has several senses) and semantic change (the process of acquiring, losing, or changing senses), and current systems perform poorly on these languages. We develop GASC, a dynamic semantic change model that leverages categorical metadata about the texts' genre to boost inference and uncover the evolution of meanings in Ancient Greek corpora. In a new evaluation framework, our model achieves improved predictive performance compared to the state of the art.