基于标签引导神经主题模型的标签语义扩展
Label Semantic Expansion via Label Guided Neural Topic Modeling
- Division of Artificial Intelligence, Lingnan University（岭南大学人工智能学院）
- School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
- School of Science and Technology, Hong Kong Metropolitan University（香港都会大学科技学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出LGNTM模型，通过“主题服务于标签”的标签语义扩展方法，在多任务实验中展现出优异的标签-主题对齐、标签扩展等性能。
AI中文摘要：
主题模型被广泛用于内容分析，用户通常围绕预定义标签而非无序潜在主题分析语料库。现有感知标签的主题模型主要遵循“标签服务于主题”视角，用标签指导主题学习，但学到的主题无法直接用于以标签为中心的分析。本文探索反向的“主题服务于标签”视角，将其实例化为标签语义扩展（Label Semantic Expansion, LSE），该方法用语料库支撑的描述性主题词丰富稀疏的标签表示。为在LSE中有效利用主题，本文提出标签引导神经主题模型（Label-Guided Neural Topic Model, LGNTM），该模型学习与标签对齐的专用主题，将其锚定在词汇和文档语义空间中，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，证明其在互补评估维度上的整体性能优异。
英文摘要：
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.
