Problem
Research questions and friction points this paper is trying to address.
Label Semantic Expansion
Topic Modeling
Label-aware
Content Analysis
Label Semantic Expansion via Label Guided Neural Topic Modeling
🤖 AI Summary
为解决标签中心分析中标签表示稀疏问题，提出通过标签引导神经主题模型（LGNTM）来丰富标签语义，提高标签-主题对齐和分类性能。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Label Semantic Expansion
Label-Guided Neural Topic Model
topic-for-labels perspective
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