Andrew McCallum

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Kamal NigamGoogleE-mail confirmado em kamalnigam.com
Charles SuttonGoogle, University of EdinburghE-mail confirmado em inf.ed.ac.uk
Fernando PereiraVP, GoogleE-mail confirmado em google.com
David MimnoAssociate Professor, Cornell UniversityE-mail confirmado em cornell.edu
Emma StrubellAssistant Professor, Carnegie Mellon UniversityE-mail confirmado em cs.cmu.edu
Sebastian RiedelHonorary Professor @ University College London, Researcher @ DeepMindE-mail confirmado em cs.ucl.ac.uk
Aron CulottaAssociate Professor of Computer Science, Tulane UniversityE-mail confirmado em tulane.edu
Nicholas MonathResearch Scientist, GoogleE-mail confirmado em google.com
Xuerui WangUber Technologies, Inc.E-mail confirmado em uber.com
Rajarshi DasAWS AI LabsE-mail confirmado em cs.washington.edu
Manzil ZaheerGoogle ResearchE-mail confirmado em cmu.edu
Michael WickUniversity of MassachusettsE-mail confirmado em cs.umass.edu
Tom MitchellFounders University Professor of Machine Learning, Carnegie Mellon UniversityE-mail confirmado em cs.cmu.edu
Luke VilnisResearch Scientist, Google DeepMindE-mail confirmado em google.com
Sameer SinghProfessor, UC Irvine | CTO, Spiffy AIE-mail confirmado em uci.edu
David BelangerResearch Scientist, Google BrainE-mail confirmado em google.com
Pat VergaCohereE-mail confirmado em cohere.com
Haw-Shiuan ChangUMass AmherstE-mail confirmado em cs.umass.edu
Hanna WallachPartner Research Manager, Microsoft ResearchE-mail confirmado em dirichlet.net
Dayne FreitagProgram Director, SRI InternationalE-mail confirmado em ai.sri.com

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Andrew McCallum

Distinguished Professor of Computer Science, University of Massachusetts Amherst

E-mail confirmado em cs.umass.edu - Página inicial

Machine Learning Natural Language Processing Artificial Intelligence Peer Review


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
Conditional random fields: Probabilistic models for segmenting and labeling sequence data J Lafferty, A McCallum, F Pereira Icml 1 (2), 3, 2001	19073	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	5947	1998
Text classification from labeled and unlabeled documents using EM K Nigam, AK McCallum, S Thrun, T Mitchell Machine learning 39, 103-134, 2000	4353	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	4059	2020
Mallet: A machine learning for languagetoolkit AK McCallum http://mallet. cs. umass. edu, 2002	3242	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	2672	2011
Maximum entropy Markov models for information extraction and segmentation. A McCallum, D Freitag, FCN Pereira Icml 17 (2000), 591-598, 2000	2150	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	1949	2006
Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons A McCallum CoNLL, 2003	1731	2003
Toward optimal active learning through monte carlo estimation of error reduction N Roy, A McCallum Icml, williamstown 2 (441-448), 4, 2001	1722	2001
Automating the construction of internet portals with machine learning AK McCallum, K Nigam, J Rennie, K Seymore Information Retrieval 3, 127-163, 2000	1678	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	1622	2010
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	1620	2000
An introduction to conditional random fields C Sutton, A McCallum Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012	1567	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	1424	1999
An introduction to conditional random fields for relational learning C Sutton Introduction to statistical relational learning, 2007	1345	2007
Employing EM and Pool-Based Active Learning for Text Classification. AK McCallum, K Nigam ICML 98, 350-358, 1998	1300	1998
Distributional clustering of words for text classification LD Baker, AK McCallum Proceedings of the 21st annual international ACM SIGIR conference on …, 1998	1132	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	1090	1998
Rethinking LDA: Why priors matter H Wallach, D Mimno, A McCallum Advances in neural information processing systems 22, 2009	1020	2009

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