Data to 7 Oct 2026

Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!

arXiv 2004.14914 ↗First posted 30 Apr 2020cs.CLcited by 73

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Abstract (arXiv)

Topic models are a useful analysis tool to uncover the underlying themes within document collections. The dominant approach is to use probabilistic topic models that posit a generative story, but in this paper we propose an alternative way to obtain topics: clustering pre-trained word embeddings while incorporating document information for weighted clustering and reranking top words. We provide benchmarks for the combination of different word embeddings and clustering algorithms, and analyse their performance under dimensionality reduction with PCA. The best performing combination for our approach performs as well as classical topic models, but with lower runtime and computational complexity.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2020.

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Citing papers per year: 2021: 7; 2022: 15; 2023: 18; 2024: 17; 2025: 10; 2026: 6.

73 papers.

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2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
2023How Do Transformers Learn Topic Structure: Towards a Mechanistic Understandingcited by 85
2021Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Wordscited by 63
2023Effective Neural Topic Modeling with Embedding Clustering Regularizationcited by 46
2022Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topicscited by 35
2022Topic Discovery via Latent Space Clustering of Pretrained Language Model Representationscited by 34
2024FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Modelcited by 34
2022Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networkscited by 24
2023Prompting Large Language Models for Topic Modelingcited by 24
2024A Survey on Neural Topic Models: Methods, Applications, and Challengescited by 18
2021Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddingscited by 12
2022Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contextscited by 12

38 more

2024LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Modelscited by 11
2021Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLPcited by 10
2022Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Mediacited by 10
2024Neural Topic Modeling with Large Language Models in the Loopcited by 10
2024Voices in a Crowd: Searching for Clusters of Unique Perspectivescited by 9
2024NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularizationcited by 9
2021Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Researchcited by 7
2022Seed-Guided Topic Discovery with Out-of-Vocabulary Seedscited by 7
2023Deep de Finetti: Recovering Topic Distributions from Large Language Modelscited by 7
2024GPTopic: Dynamic and Interactive Topic Representationscited by 7
2022Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gitacited by 6
2022Learning to Collide: Recommendation System Model Compression with Learned Hash Functionscited by 5
2023Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embeddingcited by 5
2023Topic-VQ-VAE: Leveraging Latent Codebooks for Flexible Topic-Guided Document Generationcited by 5
2024Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMscited by 5
2022Searching for Structure in Unfalsifiable Claimscited by 4
2023Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicroncited by 4
2023Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Personcited by 4
2023Evaluating Dynamic Topic Modelscited by 4
2024Self-supervised Topic Taxonomy Discovery in the Box Embedding Spacecited by 4
2025Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluationcited by 4
2021Analysis and Prediction of NLP Models Via Task Embeddingscited by 3
2022Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per classcited by 3
2023Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networkscited by 3
2023REAL: A Representative Error-Driven Approach for Active Learningcited by 3
2024Human-interpretable clustering of short-text using large language modelscited by 3
2024CAST: Corpus-Aware Self-similarity Enhanced Topic modellingcited by 3
2024Refining Dimensions for Improving Clustering-based Cross-lingual Topic Modelscited by 3
2025ProxAnn: Use-Oriented Evaluations of Topic Models and Document Clusteringcited by 3
2025Sparse Autoencoders are Topic Modelscited by 3
2021Clustering Vietnamese Conversations From Facebook Page To Build Training Dataset For Chatbotcited by 2
2022Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Postscited by 2
2021GraphTMT: Unsupervised Graph-based Topic Modeling from Video Transcriptscited by 1
2022ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholdscited by 1
2023Topics in Contextualised Attention Embeddingscited by 1
2023Topics in the Haystack: Extracting and Evaluating Topics beyond Coherencecited by 1
2023TopicGPT: A Prompt-based Topic Modeling Frameworkcited by 1
2024Probabilistic Topic Modelling with Transformer Representationscited by 1

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