David Mimno

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Andrew McCallumDistinguished Professor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
Hanna WallachPartner Research Manager, Microsoft ResearchVerified email at dirichlet.net
Alexandra SchofieldAssistant Professor, Harvey Mudd CollegeVerified email at cs.hmc.edu
Maria AntoniakPioneer Centre for AI, University of CopenhagenVerified email at colorado.edu
Moontae LeeAssistant Professor of Information Decision Sciences, UIC Business School, University of Illinois at ChicagoVerified email at uic.edu
Laure ThompsonPrinceton UniversityVerified email at princeton.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Edmund M TalleyNational Institutes of HealthVerified email at nih.gov
Gregory YauneyCornell UniversityVerified email at cornell.edu
David A. SmithAssociate Professor, College of Computer and Information Science, Northeastern UniversityVerified email at ccs.neu.edu
Jordan Boyd-GraberFull Professor, University of MarylandVerified email at umiacs.umd.edu
Yoni HalpernGoogleVerified email at google.com
Sanjeev AroraProfessor of Computer Science, Princeton UniversityVerified email at cs.princeton.edu
Ankur MoitraNorbert Wiener Professor of Mathematics, MITVerified email at mit.edu
Eric P. S. BaumerLehigh UniversityVerified email at lehigh.edu
Shion GuhaUniversity of TorontoVerified email at utoronto.ca
Jack Hesselsamaya.aiVerified email at samaya.ai
Thorsten JoachimsProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Tobias SchnabelResearcher, MicrosoftVerified email at cornell.edu
Måns MagnussonDepartment of Statistics, Uppsala University, SwedenVerified email at statistik.uu.se

David Mimno

Associate Professor, Cornell University

Verified email at cornell.edu - Homepage

Machine Learning Text Mining Topic Modeling Digital Humanities


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Optimizing semantic coherence in topic models D Mimno, H Wallach, E Talley, M Leenders, A McCallum Proceedings of the 2011 conference on empirical methods in natural language …, 2011	2725	2011
Evaluation methods for topic models HM Wallach, I Murray, R Salakhutdinov, D Mimno Proceedings of the 26th annual international conference on machine learning …, 2009	1305	2009
Rethinking LDA: Why priors matter H Wallach, D Mimno, A McCallum Advances in neural information processing systems 22, 2009	1028	2009
Evaluation methods for unsupervised word embeddings T Schnabel, I Labutov, D Mimno, T Joachims Proceedings of the 2015 conference on empirical methods in natural language …, 2015	801	2015
Topic models conditioned on arbitrary features with dirichlet-multinomial regression D Mimno arXiv preprint arXiv:1206.3278, 2012	603	2012
Efficient methods for topic model inference on streaming document collections L Yao, D Mimno, A McCallum Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	572	2009
A practical algorithm for topic modeling with provable guarantees S Arora, R Ge, Y Halpern, D Mimno, A Moitra, D Sontag, Y Wu, M Zhu International conference on machine learning, 280-288, 2013	567	2013
Polylingual topic models D Mimno, H Wallach, J Naradowsky, DA Smith, A McCallum Proceedings of the 2009 conference on empirical methods in natural language …, 2009	489	2009
Expertise modeling for matching papers with reviewers D Mimno, A McCallum Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007	404	2007
Applications of topic models J Boyd-Graber, Y Hu, D Mimno Foundations and Trends® in Information Retrieval 11 (2-3), 143-296, 2017	403	2017
Mixtures of hierarchical topics with pachinko allocation D Mimno, W Li, A McCallum Proceedings of the 24th international conference on Machine learning, 633-640, 2007	321	2007
Significant themes in 19th-century literature ML Jockers, D Mimno Poetics 41 (6), 750-769, 2013	296	2013
Care and feeding of topic models: Problems, diagnostics, and improvements J Boyd-Graber, D Mimno, D Newman Handbook of mixed membership models and their applications 225255, 2014	286	2014
Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence? EPS Baumer, D Mimno, S Guha, E Quan, GK Gay Journal of the Association for Information Science and Technology 68 (6 …, 2017	284	2017
Pulling out the stops: Rethinking stopword removal for topic models A Schofield, M Magnusson, D Mimno Proceedings of the 15th Conference of the European Chapter of the …, 2017	255	2017
Evaluating the stability of embedding-based word similarities M Antoniak, D Mimno Transactions of the Association for Computational Linguistics 6, 107-119, 2018	253	2018
Comparing apples to apple: The effects of stemmers on topic models A Schofield, D Mimno Transactions of the Association for Computational Linguistics 4, 287-300, 2016	246	2016
Sparse stochastic inference for latent Dirichlet allocation D Mimno, M Hoffman, D Blei arXiv preprint arXiv:1206.6425, 2012	197	2012
Low-dimensional embeddings for interpretable anchor-based topic inference D Mimno, M Lee Proceedings of the 2014 conference on empirical methods in natural language …, 2014	186	2014
Computational historiography: Data mining in a century of classics journals D Mimno Journal on Computing and Cultural Heritage (JOCCH) 5 (1), 1-19, 2012	169	2012

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