mlsanityData to 14 Oct 2026

Topic Modeling in Embedding Spaces

arXiv 1907.04907 ↗First posted 8 Jul 2019cs.IR cs.CL cs.LGcited by 224

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Abstract (arXiv)

Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the Embedded Topic Model (ETM), a generative model of documents that marries traditional topic models with word embeddings. In particular, it models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic. To fit the ETM, we develop an efficient amortized variational inference algorithm. The ETM discovers interpretable topics even with large vocabularies that include rare words and stop words. It outperforms existing document models, such as latent Dirichlet allocation (LDA), in terms of both topic quality and predictive performance.

Key facts. Cited by 224 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jul 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 27; 2022: 28; 2023: 50; 2024: 41; 2025: 44; 2026: 34.

224 papers.

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  1. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  2. 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
  3. 2021Topic Modelling Meets Deep Neural Networks: A Surveycited by 51
  4. 2021Consistency Regularization for Variational Auto-Encoderscited by 51
  5. 2023Effective Neural Topic Modeling with Embedding Clustering Regularizationcited by 46
  6. 2021Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Explorationcited by 44
  7. 2023GFlowNet-EM for learning compositional latent variable modelscited by 40
  8. 2022Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learningcited by 38
  9. 2022Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddingscited by 36
  10. 2021Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similaritycited by 35
  11. 2024FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Modelcited by 35
  12. 2022Topic Discovery via Latent Space Clustering of Pretrained Language Model Representationscited by 34
38 more
  1. 2023MasakhaNEWS: News Topic Classification for African languagescited by 31
  2. 2021Sequence-to-Sequence Learning with Latent Neural Grammarscited by 30
  3. 2023LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptionscited by 29
  4. 2023Prompting Large Language Models for Topic Modelingcited by 25
  5. 2021Sawtooth Factorial Topic Embeddings Guided Gamma Belief Networkcited by 23
  6. 2023Towards the TopMost: A Topic Modeling System Toolkitcited by 21
  7. 2023Contextualized Topic Coherence Metricscited by 19
  8. 2024On the Affinity, Rationality, and Diversity of Hierarchical Topic Modelingcited by 18
  9. 2024A Survey on Neural Topic Models: Methods, Applications, and Challengescited by 18
  10. 2023vONTSS: vMF based semi-supervised neural topic modeling with optimal transportcited by 16
  11. 2023DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLMcited by 16
  12. 2024Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusioncited by 16
  13. 2022HyperMiner: Topic Taxonomy Mining with Hyperbolic Embeddingcited by 15
  14. 2024Navigating the Path of Writing: Outline-guided Text Generation with Large Language Modelscited by 15
  15. 2022Neural Topic Modeling of Psychotherapy Sessionscited by 13
  16. 2021Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddingscited by 12
  17. 2022Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contextscited by 12
  18. 2024Topic Modeling as Multi-Objective Contrastive Optimizationcited by 12
  19. 2022Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Redditcited by 11
  20. 2022Are Neural Topic Models Broken?cited by 11
  21. 2024LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Modelscited by 11
  22. 2024Neural Topic Modeling with Large Language Models in the Loopcited by 10
  23. 2021TopicNet: Semantic Graph-Guided Topic Discoverycited by 9
  24. 2021Keyword Assisted Embedded Topic Modelcited by 9
  25. 2022Experiments on Generalizability of BERTopic on Multi-Domain Short Textcited by 9
  26. 2024NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularizationcited by 9
  27. 2021Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modelingcited by 8
  28. 2021Integrating topic modeling and word embedding to characterize violent deathscited by 8
  29. 2022SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learningcited by 8
  30. 2022The future is different: Large pre-trained language models fail in prediction taskscited by 8
  31. 2023Neural Dynamic Focused Topic Modelcited by 8
  32. 2023Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Modelscited by 8
  33. 2024COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modelingcited by 8
  34. 2024Semantic-Driven Topic Modeling Using Transformer-Based Embeddings and Clustering Algorithmscited by 8
  35. 2025Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoderscited by 8
  36. 2025A Survey on Retrieval And Structuring Augmented Generation with Large Language Modelscited by 8
  37. 2022Seed-Guided Topic Discovery with Out-of-Vocabulary Seedscited by 7
  38. 2022Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlookcited by 7

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