Topic · Topic Modeling
Most cited papers in Topic Modeling
What are the most cited papers in Topic Modeling?
The most cited paper in Topic Modeling is "Exploiting Generative AI to Scale up Intelligent Tutoring Systems" (Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra et al., 2023), with 79,061 citations. Next come "AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale" (46,030) and "Glove: Global Vectors for Word Representation" (34,197).
Showing 50 of 94 papers. Every paper with more than 2,725 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Exploiting Generative AI to Scale up Intelligent Tutoring Systems Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) | 2023 | 79,061 |
| 2 | AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale Kefallinos, Dionysios, Alexandris, Georgios, Maras, Alexis et al. · arXiv (Cornell University) | 11 Oct 2018 | 46,030 |
| 3 | Glove: Global Vectors for Word Representation Jeffrey Pennington, Richard Socher, Christopher D. Manning | 2014 | 34,197 |
| 4 | Latent dirichlet allocation David M. Blei, Andrew Y. Ng, Michael I. Jordan · Journal of Machine Learning Research | Mar 2003 | 27,026 |
| 5 | Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation Kyunghyun Cho, Bart van Merriënboer, Çağlar Gülçehre et al. | 2014 | 25,290 |
| 6 | Efficient Estimation of Word Representations in Vector Space Tomáš Mikolov, Kai Chen, Greg S. Corrado et al. · arXiv (Cornell University) | 16 Jan 2013 | 18,196 |
| 7 | Distributed Representations of Words and Phrases and their Compositionality Tomáš Mikolov, Ilya Sutskever, Kai Chen et al. · arXiv (Cornell University) | 16 Oct 2013 | 18,034 |
| 8 | Neural Machine Translation by Jointly Learning to Align and Translate Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio · arXiv (Cornell University) | Sep 2014 | 14,616 |
| 9 | Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics Rivo Kaugeranna, Eliina Kaugeranna, Aion, (Claude Sonnet 4.6) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) | 2023 | 14,371 |
| 10 | WordNet George Armitage Miller · Communications of the ACM | Nov 1995 | 14,290 |
| 11 | Convolutional Neural Networks for Sentence Classification Yoon Kim | 2014 | 14,059 |
| 12 | Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc Viet Le · arXiv (Cornell University) | 10 Sep 2014 | 13,337 |
| 13 | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Nils Reimers, Iryna Gurevych | 2019 | 12,029 |
| 14 | Finding Structure in Time Jeffrey L. Elman · Cognitive Science | Mar 1990 | 11,110 |
| 15 | Enriching Word Vectors with Subword Information Piotr Bojanowski, Édouard Grave, Armand Joulin et al. · Transactions of the Association for Computational Linguistics | Dec 2017 | 9,975 |
| 16 | Foundations of statistical natural language processing Christopher D. Manning, Hinrich Schütze | 1999 | 9,960 |
| 17 | Term-weighting approaches in automatic text retrieval Gerard Salton, Chris Buckley · Information Processing & Management | 1988 | 9,789 |
| 18 | DeepWalk Bryan Perozzi, Rami Al-Rfou, Steven Skiena | 22 Aug 2014 | 8,705 |
| 19 | Effective Approaches to Attention-based Neural Machine Translation Thang Luong, Hieu T. T. L. Pham, Christopher D. Manning | 2015 | 8,653 |
| 20 | Transformers: State-of-the-Art Natural Language Processing Thomas Wolf et al. | 2020 | 8,360 |
| 21 | Attention Is All You Need Ashish Vaswani, Noam Shazeer, Niki Jitendra Parmar et al. | 23 Aug 2025 | 7,660 |
| 22 | BioBERT: a pre-trained biomedical language representation model for biomedical text mining Jinhyuk Lee, Wonjin Yoon, Sungdong Kim et al. · Bioinformatics | 5 Sep 2019 | 7,445 |
| 23 | The Stanford CoreNLP Natural Language Processing Toolkit Christopher D. Manning, Mihai Surdeanu, John T. Bauer et al. | 2014 | 7,297 |
| 24 | Thumbs up? Bo Pang, Lillian Lee, Shivakumar Vaithyanathan | 2002 | 7,061 |
| 25 | Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Richard Socher, Alex Perelygin, Jean Y. Wu et al. | 2013 | 6,886 |
| 26 | On the Properties of Neural Machine Translation: Encoder–Decoder Approaches Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau et al. | 2014 | 6,731 |
| 27 | ChatGPT for good? On opportunities and challenges of large language models for education Enkelejda Kasneci et al. · Learning and Individual Differences | 9 Mar 2023 | 6,643 |
| 28 | On the Dangers of Stochastic Parrots Emily M. Bender, Timnit Gebru, Angelina McMillan-Major et al. | Mar 2021 | 6,615 |
| 29 | Finding scientific topics Thomas L. Griffiths, Mark Steyvers · Proceedings of the National Academy of Sciences | 6 Apr 2004 | 6,099 |
| 30 | VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text Cecelia Hutto, Éric Gilbert · Proceedings of the International AAAI Conference on Web and Social Media | 16 May 2014 | 6,006 |
| 31 | Graph neural networks: A review of methods and applications Jie Zhou, Ganqu Cui, Shengding Hu et al. · AI Open | 2020 | 5,883 |
| 32 | Framewise phoneme classification with bidirectional LSTM and other neural network architectures Alex M Graves, Jürgen Schmidhuber · Neural Networks | Jul 2005 | 5,668 |
| 33 | Probabilistic topic models David M. Blei · Communications of the ACM | 23 Mar 2012 | 5,645 |
| 34 | Recurrent neural network based language model Tomáš Mikolov, Martin Karafiát, Lukáš Burget et al. | 26 Sep 2010 | 5,458 |
| 35 | Modeling Relational Data with Graph Convolutional Networks Michael Schlichtkrull, Thomas Kipf, Peter Bloem et al. · Lecture notes in computer science | 2018 | 5,280 |
| 36 | A unified architecture for natural language processing Ronan Collobert, Jason Weston | 2008 | 5,252 |
| 37 | Natural Language Processing (almost) from Scratch Ronan Collobert, Jason Weston, Léon Bottou et al. · arXiv (Cornell University) | 2 Mar 2011 | 5,163 |
| 38 | Distributed Representations of Sentences and Documents Quoc Viet Le, Tomáš Mikolov · arXiv (Cornell University) | 16 May 2014 | 5,119 |
| 39 | GroupLens Paul Resnick, Neophytos Iacovou, Mitesh Suchak et al. | 1994 | 5,058 |
| 40 | An introduction to latent semantic analysis Thomas K. Landauer, Peter W. Foltz, Darrell Laham · Discourse Processes | 1998 | 4,903 |
| 41 | Hierarchical Attention Networks for Document Classification Zichao Yang, Diyi Yang, Chris Dyer et al. | 2016 | 4,899 |
| 42 | Moses Philipp Koehn, Richard Zens, Chris Dyer et al. | 2007 | 4,887 |
| 43 | LINE Jian Tang, Meng Qu, Mingzhe Wang et al. | 18 May 2015 | 4,765 |
| 44 | DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter Schmidt, Wilma Johanna, Grangel-González, Irlan, Paschke, Adrian et al. · arXiv (Cornell University) | 2 Oct 2019 | 4,596 |
| 45 | Neural Architectures for Named Entity Recognition Guillaume Lample, Miguel Ballesteros, Sandeep K. Subramanian et al. | 2016 | 4,507 |
| 46 | Survey of Hallucination in Natural Language Generation Ziwei Ji, Nayeon Lee, Rita Frieske et al. · ACM Computing Surveys | 17 Nov 2022 | 4,501 |
| 47 | A theory of memory retrieval. Roger Ratcliff · Psychological Review | Mar 1978 | 4,280 |
| 48 | The mathematics of statistical machine translation: parameter estimation Peter F. Brown, Vincent J. Della Pietra, Stephen A. Della Pietra et al. | Jun 1993 | 4,107 |
| 49 | GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Wang, Amanpreet Singh, Julian Michael et al. | 2018 | 4,101 |
| 50 | Probabilistic Latent Semantic Indexing Thomas Frank Hofmann · ACM SIGIR Forum | 2 Aug 2017 | 4,050 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.