An embedded diachronic sense change model with a case study from ancient Greek
本文提出EDiSC模型，将词嵌入与历时语义变化模型结合，用于分析古希腊语词汇（如“kosmos”）的语义演变，相比现有模型在预测精度、不确定性量化和计算效率上均有提升。
Word meanings change over time, and word senses evolve, emerge or die out in the process. For ancient languages, where the corpora are often small and sparse, modelling such changes accurately proves challenging, and quantifying uncertainty in sense-change estimates consequently becomes important. GASC (Genre-Aware Semantic Change) and DiSC (Diachronic Sense Change) are existing generative models that have been used to analyse sense change for target words from an ancient Greek text corpus, using unsupervised learning without the help of any pre-training. These models represent the senses of a given target word such as “kosmos” (meaning decoration, order or world) as distributions over context words, and sense prevalence as a distribution over senses. The models are fitted using Markov Chain Monte Carlo (MCMC) methods to measure temporal changes in these representations. This paper introduces EDiSC, an Embedded DiSC model, which combines word embeddings with DiSC to provide superior model performance. It is shown empirically that EDiSC offers improved predictive accuracy, ground-truth recovery and uncertainty quantification, as well as better sampling efficiency and scalability properties with MCMC methods. The challenges of fitting these models are also discussed. • Bayesian topic-based sense-change models are enhanced using word embeddings. • Benefits include accuracy, true-model recovery, sampling efficiency and scalability. • Posterior multimodality is solved using carefully constructed MCMC. • Combination of user input and WAIC guides model-selection choices. • Credible sets obtained in unsupervised and supervised settings are comparable.