Dynamic topic models have been proposed as a tool for historical analysis,
but traditional approaches have had limited usefulness, being difficult to
configure, interpret, and evaluate. In this work, we experiment with a recent
approach for dynamic topic modeling using BERT embeddings. We compare topic
models built using traditional statistical models (LDA and NMF) and the
BERT-based model, modeling topics over the entire surviving corpus of Roman
literature. We find that while quantitative metrics prefer statistical models,
qualitative evaluation finds better insights from the neural model.
Furthermore, the neural topic model is less sensitive to hyperparameter
configuration and thus may make dynamic topic modeling more viable for
historical researchers.

使用 BERT 嵌入实现的动态主题模型与传统的统计模型（LDA 和 NMF）进行比较，模型主题遍布整个罗马文学存档，定量评估偏向统计模型，而定性评估对神经模型有更好的洞察力，此外，神经主题模型对超参数配置不敏感，使动态主题建模对历史研究人员更具可行性。

历史是生活的导师：利用神经嵌入进行古罗马文学的动态主题建模

Historia Magistra Vitae: Dynamic Topic Modeling of Roman Literature  using Neural Embeddings

Digital humanities research has flourished due to the diverse artifacts
available in cultural heritage databases. However, over-reliance on a single
artifact type can result in poor contextualization and a constrained
understanding of historical context. We collaborated with art historians to
examine handscrolls, a form of traditional Chinese painting which offers a
wealth of data for historical analysis and provides a unique opportunity for
understanding history through artwork. We propose ScrollTimes, a visual
analysis system for tracing handscroll historic context by linking multiple
data sources. Specifically, a unique layout is developed for efficiently
viewing long handscrolls. Using image processing techniques and language
models, we extract, verify, and supplement elements in handscrolls with
different cultural heritage databases. Furthermore, interactive biographies are
constructed for handscrolls to uncover their historical narratives, provenance
trajectories, and artistic legacies. Validated through case studies and expert
interviews, our approach offers a window into history, fostering a holistic
understanding of handscroll provenance and historical significance.

通过使用手卷绘画作品，我们提出了一种名为 ScrollTimes 的系统，它结合了图像处理技术和语言模型，可用于追踪历史背景并揭示手卷绘画作品的历史性价值。