Andrew McCallum

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Kamal NigamGoogleEmail confirmado em kamalnigam.com
Charles SuttonGoogle, University of EdinburghEmail confirmado em inf.ed.ac.uk
Fernando PereiraVP, GoogleEmail confirmado em google.com
David MimnoAssociate Professor, Cornell UniversityEmail confirmado em cornell.edu
Emma StrubellAssistant Professor, Carnegie Mellon UniversityEmail confirmado em cs.cmu.edu
Sebastian RiedelHonorary Professor @ University College London, Researcher @ DeepMindEmail confirmado em cs.ucl.ac.uk
Aron CulottaAssociate Professor of Computer Science, Tulane UniversityEmail confirmado em tulane.edu
Nicholas MonathResearch Scientist, GoogleEmail confirmado em google.com
Rajarshi DasAWS AI LabsEmail confirmado em cs.washington.edu
Xuerui WangUber Technologies, Inc.Email confirmado em uber.com
Manzil ZaheerGoogle ResearchEmail confirmado em cmu.edu
Michael WickUniversity of MassachusettsEmail confirmado em cs.umass.edu
Luke VilnisResearch Scientist, Google DeepMindEmail confirmado em google.com
Tom MitchellFounders University Professor of Machine Learning, Carnegie Mellon UniversityEmail confirmado em cs.cmu.edu
Sameer SinghProfessor, UC Irvine | CTO, Spiffy AIEmail confirmado em uci.edu
David BelangerResearch Scientist, Google BrainEmail confirmado em google.com
Pat VergaCohereEmail confirmado em cohere.com
Hanna WallachVP & Distinguished Scientist, Microsoft ResearchEmail confirmado em dirichlet.net
Haw-Shiuan ChangUMass AmherstEmail confirmado em cs.umass.edu
Dayne FreitagProgram Director, SRI InternationalEmail confirmado em ai.sri.com

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Andrew McCallum

Distinguished Professor of Computer Science, University of Massachusetts Amherst

Email 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	19260	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	6036	1998
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	4475	2020
Text classification from labeled and unlabeled documents using EM K Nigam, AK McCallum, S Thrun, T Mitchell Machine learning 39, 103-134, 2000	4390	2000
Mallet: A machine learning for languagetoolkit AK McCallum http://mallet. cs. umass. edu, 2002	3259	2002
An introduction to conditional random fields C Sutton, A McCallum Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012	2882*	2012
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	2777	2011
Maximum entropy Markov models for information extraction and segmentation. A McCallum, D Freitag, FCN Pereira Icml 17 (2000), 591-598, 2000	2166	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	1950	2006
Automating the construction of internet portals with machine learning AK McCallum, K Nigam, J Rennie, K Seymore Information Retrieval 3, 127-163, 2000	1769	2000
Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons A McCallum, W Li Proceedings of the seventh conference on Natural language learning at HLT …, 2003	1746	2003
Toward optimal active learning through sampling estimation of error reduction N Roy, A McCallum ICML 1 (3), 5, 2001	1742	2001
Modeling relations and their mentions without labeled text S Riedel, L Yao, A McCallum Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010	1669	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	1623	2000
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	1435	1999
Employing EM and Pool-Based Active Learning for Text Classification. AK McCallum, K Nigam ICML 98, 350-358, 1998	1303	1998
Distributional clustering of words for text classification LD Baker, AK McCallum Proceedings of the 21st annual international ACM SIGIR conference on …, 1998	1134	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	1109	1998
Rethinking LDA: Why priors matter H Wallach, D Mimno, A McCallum Advances in neural information processing systems 22, 2009	1025	2009
Pachinko allocation: DAG-structured mixture models of topic correlations W Li, A McCallum Proceedings of the 23rd international conference on Machine learning, 577-584, 2006	974	2006

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