用主题模型扩展标签语义，让稀疏标签更丰富可解释。
Label Semantic Expansion via Label Guided Neural Topic Modeling
- 从标签出发构建对齐主题，反向实现标签增强
- 在词和文档语义空间中保持标签与主题一致
- 适合需要精准标签理解的文本分析任务
主题模型广泛用于内容分析，但用户常围绕预定义标签而非无序隐含主题进行研究。现有标签感知主题模型多采用‘标签引导主题’思路，学习到的主题无法直接用于标签中心分析。本文提出反向视角——‘主题服务标签’，并构建标签语义扩展（LSE）框架，通过语料支撑的主题词丰富稀疏标签表示。为有效利用主题，提出标签引导神经主题模型（LGNTM），学习与标签对齐的主题，将其锚定于词汇和文档语义空间，并保持主题结构与标签结构的一致性。在标签-主题对齐、标签扩展、主题质量及下游分类任务上的实验表明，LGNTM在多个互补评估维度上均表现优异。
原文摘要 · Abstract (English)
Topic models are widely used for content analysis, where users often analyze corpora around predefined labels rather than unordered latent topics. Existing label-aware topic models mainly follow a labels-for-topics perspective, using labels to guide topic learning, while the learned topics are not directly usable for label-centered analysis. We explore the reverse topics-for-labels perspective and instantiate it as Label Semantic Expansion (LSE), which enriches sparse label representations with corpus-grounded descriptive topic words. To exploit topics in LSE effectively, we propose a Label-Guided Neural Topic Model (LGNTM), which learns dedicated label-aligned topics, grounds them in lexical and document semantic spaces, and preserves consistency between topic structures and label structures. Experiments on label-topic alignment, label expansion, topic quality, and downstream classification demonstrate strong overall performance across complementary evaluation dimensions.
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