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Topic modeling is a widely used technique for uncovering thematic structures from large text corpora.
Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003 · 2003
Earlier work this paper cites.
Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America
T Griffiths and M Steyvers. 2004 · 2004
Earlier work this paper cites.
Normalized (pointwise) mutual information in collocation extraction
Gerlof Bouma. 2009 · 2009
Earlier work this paper cites.
On finding the natural number of topics with latent dirichlet allocation: Some observations. In Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I 14 . Springer, 391–402
Rajkumar Arun, Venkatasubramaniyan Suresh, CE Veni Madhavan, and MN Narasimha Murthy. 2010 · 2010
Earlier work this paper cites.
Probabilistic topic models
David M Blei. 2012 · 2012
Earlier work this paper cites.
Text as data: The promise and pitfalls of automatic content analysis methods for political texts
Justin Grimmer and Brandon M Stewart. 2013 · 2013
Earlier work this paper cites.
Accurate and effective latent concept modeling for ad hoc information retrieval
Romain Deveaud, Eric SanJuan, and Patrice Bellot. 2014 · 2014
Earlier work this paper cites.
Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics . 530–539
Jey Han Lau, David Newman, and Timothy Baldwin. 2014 · 2014
Earlier work this paper cites.
Structural topic models for open-ended survey responses
Margaret E Roberts, Brandon M Stewart, Dustin Tingley, Christopher Lucas, Jetson Leder-Luis, Shana Kushner Gadarian, Bethany Albertson, and David G Rand. 2014 · 2014
Earlier work this paper cites.
Attention is all you need
Vaswani Ashish. 2017 · 2017
Earlier work this paper cites.
NLP in Engineering Education-Demonstrating the use of Natural Language Processing Techniques for Use in Engineering Education Classrooms and Research
Sreyoshi Bhaduri. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin. 2018 · 2018
Cited alongside, same era.
Patentbert: Patent classification with fine-tuning a pre-trained bert model
Jieh-Sheng Lee and Jieh Hsiang. 2019 · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cluster quality analysis using silhouette score. In 2020 IEEE 7th international conference on data science and advanced analytics (DSAA) . IEEE, 747–748
Ketan Rajshekhar Shahapure and Charles Nicholas. 2020 · 2020
Later among the works it cites.
Ernie 2.0: A continual pre-training framework for language understanding. In Proceedings of the AAAI conference on artificial intelligence , Vol. 34. 8968–8975
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang. 2020 · 2020
Later among the works it cites.
A Semester Like No Other: Use of Natural Language Processing for Novice-Led Analysis on End-of-Semester Responses on Students’ Experience of Changing Learning Environments Due to COVID-19. In 2021 ASEE Virtual Annual Conference Content Access
Sreyoshi Bhaduri, Michelle Soledad, Tamoghna Roy, Homero Murzi, and Tamara Knott. 2021 · 2021
Later among the works it cites.
Contrastive learning for label efficient semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 10623–10633
Xiangyun Zhao, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira, and Ying Wu. 2021 · 2021
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Cited alongside, same era.
Pre-training is a hot topic: Contextualized document embeddings improve topic coherence
Federico Bianchi, Silvia Terragni, and Dirk Hovy. 2020 · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom B Brown. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel Colin. 2020 · 2020
Cited alongside, same era.
Topic modeling in embedding spaces
Adji B Dieng, Francisco JR Ruiz, and David M Blei. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Using science to support and develop employees in the tech workforce-an opportunity for multi-disciplinary pursuits in engineering education
Sreyoshi Bhaduri, Marina Dias, Amulya Mysore, Robert Pulvermacher, Amelia Rivera-Burnett, Shahriar Sadighi, and Wanqun Zhao. 2023 · 2023
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Sreyoshi Bhaduri, Satya Kapoor, Alex Gil, Anshul Mittal, and Rutu Mulkar. 2024a · 2024
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(Multi-disciplinary) Teamwork makes the (real) dream work: Pragmatic recommendations from industry for engineering classrooms
Sreyoshi Bhaduri, Kenneth Ohnemus, Jess Blackburn, Anshul Mittal, Yan Dong, Savannah Laferriere, Robert Pulvermacher, Marina Dias, Alex Gil, Shahriar Sadighi, et al · 2024
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Large language models for telecom: Forthcoming impact on the industry
Ali Maatouk, Nicola Piovesan, Fadhel Ayed, Antonio De Domenico, and Merouane Debbah. 2024 · 2024
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Is automated topic model evaluation broken? the incoherence of coherence
Alexander Hoyle, Pranav Goel, Andrew Hian-Cheong, Denis Peskov, Jordan Boyd-Graber, and Philip Resnik. 2021 · 2033
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