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.
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.
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.
MonoTM is an interpretable topic modeling framework that separates the estimation of document–topic mixtures from the generation of topic descriptors. It uses sparse autoencoders to extract dense, interpretable features for mixture estimation, then learns topic descriptors from a distinct set of corpus‑grounded semantic features. This approach preserves global topic structure while providing more meaningful, semantic‑unit descriptors than traditional top‑word lists.
arXiv:2507. 23220v2 Announce Type: replace-cross Abstract: Traditional topic models are effective at uncovering latent themes in large text collections.
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.
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.
Dynamic Embedded Topic Models (D-ETM) are extended to enable stable cross‑corpus temporal analysis by first learning a shared dynamic topic space—called the shared backbone—over a merged multi‑corpus collection. Corpus‑specific residual adaptation is then applied around this frozen backbone, allowing each corpus to specialize lexically without creating separate latent topic spaces. Experiments on three corpora spanning 97 years show that this approach yields much stronger alignment of topic trajectories (97.5 ± 0.7 % Retrieval@1) compared to full fine‑tuning or independent training with post‑hoc matching.
arXiv:2510. 16152v2 Announce Type: replace-cross Abstract: Scientific literature is increasingly fragmented by disciplinary boundaries, specialized terminology, and potentially sparse keyword systems, making it difficult to capture the evolving structure of modern science.
arXiv:2609.14256v1 Announce Type: new Abstract: Topic models are widely used to analyze public health-related social media short texts, yet their evaluation remains dominated by metrics that focus en...
arXiv:2608. 12756v1 Announce Type: cross Abstract: Adaptive latent tokenization maps a fine-grained input to a shorter sequence of continuous representations associated with input-dependent spans.
The paper introduces a new method that uses embedding-based dynamic topic modeling to detect and measure topic drift at the comment level in a large dataset. By applying pretrained language models to generate contextualized embeddings for 12.7 billion Reddit comments from 2006 to 2022, the authors identify evolving topic clusters over time using unsupervised techniques. Their approach includes scalable modifications to existing methods and a null model comparison test, revealing that politically and socially contentious topics show significant directional drift, while areas like music and sports remain relatively stable.
arXiv:2506. 10292v2 Announce Type: replace-cross Abstract: Training deep learning networks with minimal supervision has gained significant research attention due to its potential to reduce reliance on extensive labelled data.