Label Semantic Expansion via Label Guided Neural Topic Modeling

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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.30216 [cs.CL]

(or arXiv:2608.30216v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.30216 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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