Lexical semantic change for Ancient Greek and Latin

TL;DR

This paper compares Bayesian and embedding-based models for detecting semantic change in Ancient Greek and Latin, highlighting the interpretability and effectiveness of Bayesian methods in capturing historical language evolution.

Contribution

It introduces a dynamic Bayesian mixture model that incorporates genre information and systematically compares it with embedding models for semantic change detection.

Findings

Bayesian models effectively detect semantic change in classical languages.

Bayesian approaches outperform or match embedding models in interpretability and accuracy.

Genre information improves semantic change detection accuracy.

Abstract

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.