将时间感知词向量映射到动态主题模型，实现词与文档嵌入的时序对比可视化。
Visualizing Temporal Topic Embeddings with a Compass
- 提出基于指南针对齐的时间词向量扩展方法，统一建模词与文档的时序变化。
- 在多个规模的数据集上，主题相关性与多样性表现达当前最优水平。
- 可直观展示词汇使用随时间演变的过程，适合关注主题演化的研究者。
动态主题建模有助于发现潜在主题随时间的发展与变迁。然而，现有方法将文档与词的表示分离，难以构建一个能直接分析词与文档时序变化的有意义嵌入空间。本文提出将指南针对齐的时间词向量方法扩展至动态主题建模，使得词与文档嵌入可在时间维度上直接比较。该方法支持将时序词嵌入融入文档上下文进行主题可视化。在不同规模的时间数据集上的实验表明，所提方法在主题相关性与多样性方面均达到当前最优性能。同时，其生成的可视化结果聚焦于词嵌入的时序演变，兼顾全局主题演化洞察，深化了对主题动态演化的理解。
原文摘要 · Abstract (English)
Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.
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