mlsanityData to 7 Oct 2026

Neural Models for Documents with Metadata

arXiv 1705.09296 ↗First posted 25 May 2017stat.ML cs.CLcited by 46

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Abstract (arXiv)

Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information. While specialized models have been developed for particular applications, few are widely used in practice, as customization typically requires derivation of a custom inference algorithm. In this paper, we build on recent advances in variational inference methods and propose a general neural framework, based on topic models, to enable flexible incorporation of metadata and allow for rapid exploration of alternative models. Our approach achieves strong performance, with a manageable tradeoff between perplexity, coherence, and sparsity. Finally, we demonstrate the potential of our framework through an exploration of a corpus of articles about US immigration.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 May 2017.

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Citing papers per year: 2021: 10; 2022: 9; 2023: 8; 2024: 10; 2025: 7; 2026: 2.

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  1. 2021DEMix Layers: Disentangling Domains for Modular Language Modelingcited by 130
  2. 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
  3. 2022Latent Diffusion Energy-Based Model for Interpretable Text Modelingcited by 67
  4. 2021Topic Modelling Meets Deep Neural Networks: A Surveycited by 51
  5. 2021Contrastive Learning for Neural Topic Modelcited by 48
  6. 2023Effective Neural Topic Modeling with Embedding Clustering Regularizationcited by 46
  7. 2022Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learningcited by 38
  8. 2022Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddingscited by 36
  9. 2023Towards the TopMost: A Topic Modeling System Toolkitcited by 21
  10. 2024On the Affinity, Rationality, and Diversity of Hierarchical Topic Modelingcited by 18
  11. 2024A Survey on Neural Topic Models: Methods, Applications, and Challengescited by 18
  12. 2021Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classificationcited by 14
34 more
  1. 2024Topic Modeling as Multi-Objective Contrastive Optimizationcited by 12
  2. 2022Are Neural Topic Models Broken?cited by 11
  3. 2024LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Modelscited by 11
  4. 2022Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Mediacited by 10
  5. 2024Neural Topic Modeling with Large Language Models in the Loopcited by 10
  6. 2021Neural Attention-Aware Hierarchical Topic Modelcited by 6
  7. 2022Hierarchical Interpretation of Neural Text Classificationcited by 5
  8. 2023Diversity-Aware Coherence Loss for Improving Neural Topic Modelscited by 5
  9. 2021Bayesian Topic Regression for Causal Inferencecited by 4
  10. 2022Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Mediacited by 4
  11. 2025Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluationcited by 4
  12. 2023CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Modelscited by 3
  13. 2025Sparse Autoencoders are Topic Modelscited by 3
  14. 2021Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviewscited by 1
  15. 2022Neural Topic Modeling with Deep Mutual Information Estimationcited by 1
  16. 2022Classifying COVID-19 vaccine narrativescited by 1
  17. 2023Reinforcement Learning for Topic Modelscited by 1
  18. 2023Graph Contrastive Topic Modelcited by 1
  19. 2023Towards Generalising Neural Topical Representationscited by 1
  20. 2023Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notescited by 1
  21. 2024Interactive Topic Models with Optimal Transportcited by 1
  22. 2024Multilingual Topic Classification in X: Dataset and Analysiscited by 1
  23. 2024Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authorscited by 1
  24. 2024Multi-environment Topic Modelscited by 1
  25. 2025Tell, Don't Show: Leveraging Language Models' Abstractive Retellings to Model Literary Themescited by 1
  26. 2025Toward Purpose-oriented Topic Model Evaluation enabled by Large Language Modelscited by 1
  27. 2021Deep Probabilistic Graphical Modelingcited by 0
  28. 2021Variational Gaussian Topic Model with Invertible Neural Projectionscited by 0
  29. 2024Embedded Topic Models Enhanced by Wikificationcited by 0
  30. 2025Self-Supervised Learning for Neural Topic Models with Variance-Invariance-Covariance Regularizationcited by 0
  31. 2025Evaluating Negative Sampling Approaches for Neural Topic Modelscited by 0
  32. 2025Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topicscited by 0
  33. 2026A Neural Topic Method Using a Large-Language-Model-in-the-Loop for Business Researchcited by 0
  34. 2026Label Semantic Expansion via Label Guided Neural Topic Modelingcited by 0