arXiv:2507. 23220v2 Announce Type: replace-cross Abstract: Traditional topic models are effective at uncovering latent themes in large text collections.
By Carolina Zheng, Nicolas Beltran-Velez, Sweta Karlekar, Claudia Shi, Achille Nazaret, Asif Mallik, Amir Feder, David M. Blei
The paper introduces Label Semantic Expansion (LSE), a method that enriches sparse label representations by adding descriptive topic words grounded in a corpus. It proposes a Label-Guided Neural Topic Model (LGNTM) that learns label-aligned topics, integrates lexical and document semantics, and maintains consistency between topic and label structures. Experiments show that LSE and LGNTM improve label-topic alignment, label expansion, topic quality, and downstream classification performance.
By Haojia Zheng, Yuyin Lu, Juntian Huang, Fan Ou, Yanghui Rao, Haoran Xie, Fu Lee Wang
TopiCLEAR is a framework that clusters document or sentence embeddings using adaptive dimensionality reduction to uncover low‑dimensional geometric structures that correspond to human‑interpretable topics. The method is evaluated on four benchmark datasets, showing strong agreement with human annotations, especially for short and informal texts. A Twitter case study demonstrates that TopiCLEAR yields more interpretable topics than LDA, recovering both annotated topic structure and coherent sub‑topics.
By Aoi Fujita, Taichi Yamamoto, Yuri Nakayama, Ryota Kobayashi
arXiv:2602. 17907v2 Announce Type: replace-cross Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity.
By Raymond Li, Amirhossein Abaskohi, Chuyuan Li, Gabriel Murray, Giuseppe Carenini
arXiv:2602. 17907v3 Announce Type: replace-cross Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity.
By Raymond Li, Amirhossein Abaskohi, Chuyuan Li, Gabriel Murray, Giuseppe Carenini
arXiv:2608. 16269v1 Announce Type: cross Abstract: Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpretability often degrades on specialized corpora.
By Seung-Won Seo, Won Ik Cho, Yongmin Yoo