Andrew McCallum

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Kamal NigamGoogleDirección de correo verificada de kamalnigam.com
Fernando PereiraVP, GoogleDirección de correo verificada de google.com
Charles SuttonGoogle, University of EdinburghDirección de correo verificada de inf.ed.ac.uk
David MimnoAssociate Professor, Cornell UniversityDirección de correo verificada de cornell.edu
Sebastian RiedelProfessor @ University College London, Researcher @ DeepMindDirección de correo verificada de cs.ucl.ac.uk
Aron CulottaAssociate Professor of Computer Science, Tulane UniversityDirección de correo verificada de tulane.edu
Xuerui WangUber Technologies, Inc.Dirección de correo verificada de uber.com
Emma StrubellAssistant Professor, Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Michael WickUniversity of MassachusettsDirección de correo verificada de cs.umass.edu
Tom MitchellFounders University Professor of Machine Learning, Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Sameer SinghAssociate Professor, University of California IrvineDirección de correo verificada de uci.edu
Luke VilnisResearch Scientist, GoogleDirección de correo verificada de google.com
Nicholas MonathResearch Scientist, GoogleDirección de correo verificada de google.com
David BelangerResearch Scientist, Google BrainDirección de correo verificada de google.com
Pat VergaResearch Scientist GoogleDirección de correo verificada de google.com
Hanna WallachPartner Research Manager, Microsoft ResearchDirección de correo verificada de dirichlet.net
Manzil ZaheerGoogle ResearchDirección de correo verificada de cmu.edu
Dayne FreitagProgram Director, SRI InternationalDirección de correo verificada de ai.sri.com
Gregory DruckGraphiteDirección de correo verificada de graphitehq.com
Gideon MannBloomberg LPDirección de correo verificada de bloomberg.net

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Andrew McCallum

Distinguished Professor of Computer Science, University of Massachusetts Amherst

Dirección de correo verificada de cs.umass.edu - Página principal

Machine Learning Natural Language Processing Information Extraction Artificial Intelligence Social Network Analysis


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Conditional random fields: Probabilistic models for segmenting and labeling sequence data J Lafferty, A McCallum, FCN Pereira	17461	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	5550	1998
Text classification from labeled and unlabeled documents using EM K Nigam, AK McCallum, S Thrun, T Mitchell Machine learning 39, 103-134, 2000	4148	2000
Mallet: A machine learning for languagetoolkit AK McCallum http://mallet. cs. umass. edu, 2002	3015	2002
An introduction to conditional random fields C Sutton, A McCallum Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012	2531	2012
Maximum entropy Markov models for information extraction and segmentation. A McCallum, D Freitag, FCN Pereira Icml 17 (2000), 591-598, 2000	2034	2000
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	2006	2011
Energy and policy considerations for deep learning in NLP E Strubell, A Ganesh, A McCallum arXiv preprint arXiv:1906.02243, 2019	1980	2019
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	1843	2006
Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons A McCallum, W Li	1595	2003
Toward optimal active learning through monte carlo estimation of error reduction N Roy, A McCallum ICML, Williamstown 2, 441-448, 2001	1547	2001
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	1541	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	1379	1999
Modeling relations and their mentions without labeled text S Riedel, L Yao, A McCallum Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010	1292	2010
Employing EM and Pool-Based Active Learning for Text Classification. A McCallum, K Nigam ICML 98, 350-358, 1998	1214	1998
Automating the construction of internet portals with machine learning AK McCallum, K Nigam, J Rennie, K Seymore Information Retrieval 3, 127-163, 2000	1205	2000
Distributional clustering of words for text classification LD Baker, AK McCallum Proceedings of the 21st annual international ACM SIGIR conference on …, 1998	1106	1998
Learning to extract symbolic knowledge from the World Wide Web M Craven, A McCallum, D PiPasquo, T Mitchell, D Freitag Carnegie-mellon univ pittsburgh pa school of computer Science, 1998	1019	1998
Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering AK McCallum CMU: Pittsburgh, PA, 1996	947*	1996
Rethinking LDA: Why priors matter H Wallach, D Mimno, A McCallum Advances in neural information processing systems 22, 2009	900	2009

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