Topic Modeling
This cluster of papers covers a wide range of advancements in natural language processing, including neural network architectures, word representation models, machine translation techniques, text classification algorithms, semantic similarity measures, named entity recognition methods, pretrained language models, sequence-to-sequence learning approaches, topic modeling strategies, and information retrieval systems.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.
Most cited
- AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
- HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case
- Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics
- Enriching Word Vectors with Subword Information
- Term-weighting approaches in automatic text retrieval
- Probabilistic topic models
Most recent
- Vibration-Language Model for Fault Diagnosis with Numerically Reliable Evidence
- DeepSolo-Sem: semantically adaptive explicit-point text spotting with a lightweight character-sequence prior
- SciRep: A Ranking-Aware Representation Model for Scientific Text
- Layer-Scoped Expert-Budget Expansion Discovers Succinct Convergence in Sparse Mixture-of-Experts Reasoning: Reducing Reasoning Tokens at Matched Accuracy on MMLU-Pro
- Layer-Scoped Expert-Budget Expansion Discovers Succinct Convergence in Sparse Mixture-of-Experts Reasoning: Reducing Reasoning Tokens at Matched Accuracy on MMLU-Pro
- Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings