Dynamic Topic Modeling for Cross-Corpus Temporal Analysis

2026-08-24

AI Summary

本文提出了一种动态嵌入主题模型框架，通过学习一个共享的主题空间并引入特定语料库的残差适应来解决跨语料库的时间分析中主题对齐不稳定的问题。

Abstract

Dynamic Embedded Topic Models (D-ETM) provide an interpretable framework for modeling temporal semantic evolution, but cross-corpus comparison remains difficult because topics are often learned independently and aligned only after training, a process that does not guarantee stable topic correspondence across corpora and time. To address this problem, we propose a D-ETM framework that first learns a common dynamic topic space over a merged multi-corpus collection, which we call the shared backbone, then introduces corpus-specific residual adaptation around the frozen backbone without creating separate latent topic spaces. This design preserves a shared topic index for cross-corpus comparison while allowing each corpus to specialize lexically. We evaluate the framework on three temporally structured corpora spanning 97 years: the Corpus of Historical American English, Harvard Business Review, and International Labour Review. Residual adaptation improves corpus-specific fit relative to the shared backbone while preserving the same-index cross-corpus topic trajectories, achieving substantially stronger alignment than full fine-tuning from the same backbone, with $97.5 \pm 0.7\%$ versus $17.9 \pm 1.1\%$ trajectory Retrieval@1, as well as stronger alignment than independent training with post-hoc Hungarian matching. These results suggest that incorporating topic alignment into the model can support more stable over-time cross-corpus comparisons while retaining corpus-specific lexical variation.

Problem

• Dynamic Topic Modeling
• Cross-Corpus Comparison
• Temporal Semantic Evolution
• Topic Alignment
• Shared Topic Space

Innovation

• Dynamic Embedded Topic Models
• shared backbone
• residual adaptation
• cross-corpus comparison
• temporal semantic evolution