Andrew McCallum

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Kamal NigamGoogleAdresse e-mail validée de kamalnigam.com
Charles SuttonGoogle, University of EdinburghAdresse e-mail validée de inf.ed.ac.uk
Fernando PereiraVP, GoogleAdresse e-mail validée de google.com
David MimnoAssociate Professor, Cornell UniversityAdresse e-mail validée de cornell.edu
Sebastian RiedelHonorary Professor @ University College London, Researcher @ DeepMindAdresse e-mail validée de cs.ucl.ac.uk
Emma StrubellAssistant Professor, Carnegie Mellon UniversityAdresse e-mail validée de cs.cmu.edu
Aron CulottaAssociate Professor of Computer Science, Tulane UniversityAdresse e-mail validée de tulane.edu
Xuerui WangUber Technologies, Inc.Adresse e-mail validée de uber.com
Nicholas MonathResearch Scientist, GoogleAdresse e-mail validée de google.com
Rajarshi DasAWS AI LabsAdresse e-mail validée de cs.washington.edu
Manzil ZaheerGoogle ResearchAdresse e-mail validée de cmu.edu
Michael WickUniversity of MassachusettsAdresse e-mail validée de cs.umass.edu
Tom MitchellFounders University Professor of Machine Learning, Carnegie Mellon UniversityAdresse e-mail validée de cs.cmu.edu
Luke VilnisResearch Scientist, Google DeepMindAdresse e-mail validée de google.com
Sameer SinghAssociate Professor, UC IrvineAdresse e-mail validée de uci.edu
David BelangerResearch Scientist, Google BrainAdresse e-mail validée de google.com
Pat VergaCohereAdresse e-mail validée de cohere.com
Haw-Shiuan ChangUMass AmherstAdresse e-mail validée de cs.umass.edu
Hanna WallachPartner Research Manager, Microsoft ResearchAdresse e-mail validée de dirichlet.net
Dayne FreitagProgram Director, SRI InternationalAdresse e-mail validée de ai.sri.com

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Andrew McCallum

Distinguished Professor of Computer Science, University of Massachusetts Amherst

Adresse e-mail validée de cs.umass.edu - Page d'accueil

Machine Learning Natural Language Processing Artificial Intelligence Peer Review


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Conditional random fields: Probabilistic models for segmenting and labeling sequence data J Lafferty, A McCallum, F Pereira Icml 1 (2), 3, 2001	18889	2001
A comparison of event models for naive bayes text classification A McCallum, K Nigam AAAI-98 workshop on learning for text categorization 752 (1), 41-48, 1998	5880	1998
Text classification from labeled and unlabeled documents using EM K Nigam, AK McCallum, S Thrun, T Mitchell Machine learning 39, 103-134, 2000	4324	2000
Energy and policy considerations for modern deep learning research E Strubell, A Ganesh, A McCallum Proceedings of the AAAI conference on artificial intelligence 34 (09), 13693 …, 2020	3757	2020
Mallet: A machine learning for languagetoolkit AK McCallum http://mallet. cs. umass. edu, 2002	3218	2002
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	2597	2011
Maximum entropy Markov models for information extraction and segmentation. A McCallum, D Freitag, FCN Pereira Icml 17 (2000), 591-598, 2000	2136	2000
Topics over time: a non-markov continuous-time model of topical trends X Wang, A McCallum Proceedings of the 12th ACM SIGKDD international conference on Knowledge …, 2006	1930	2006
Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons A McCallum, W Li	1713	2003
Toward optimal active learning through monte carlo estimation of error reduction N Roy, A McCallum Icml, williamstown 2 (441-448), 4, 2001	1697	2001
Automating the construction of internet portals with machine learning AK McCallum, K Nigam, J Rennie, K Seymore Information Retrieval 3, 127-163, 2000	1618	2000
Efficient clustering of high-dimensional data sets with application to reference matching A McCallum, K Nigam, LH Ungar Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	1614	2000
Modeling relations and their mentions without labeled text S Riedel, L Yao, A McCallum Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010	1591	2010
An introduction to conditional random fields C Sutton, A McCallum Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012	1536	2012
Using maximum entropy for text classification K Nigam, J Lafferty, A McCallum IJCAI-99 workshop on machine learning for information filtering 1 (1), 61-67, 1999	1417	1999
An introduction to conditional random fields for relational learning C Sutton Introduction to statistical relational learning, 2007	1343	2007
Employing EM and Pool-Based Active Learning for Text Classification. AK McCallum, K Nigam ICML 98, 350-358, 1998	1292	1998
Distributional clustering of words for text classification LD Baker, AK McCallum Proceedings of the 21st annual international ACM SIGIR conference on …, 1998	1130	1998
Learning to extract symbolic knowledge from the World Wide Web M Craven, D DiPasquo, D Freitag, A McCallum, T Mitchell, K Nigam, ... AAAI/IAAI 3 (3.6), 2, 1998	1083	1998
Rethinking LDA: Why priors matter H Wallach, D Mimno, A McCallum Advances in neural information processing systems 22, 2009	1011	2009

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