Andrew McCallum - Google Scholar Citations

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Citations	70733	26709
h-index	100	72
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Kamal NigamGoogleVerified email at kamalnigam.com
Charles SuttonGoogle, University of EdinburghVerified email at inf.ed.ac.uk
Fernando PereiraVP and Engineering Fellow, GoogleVerified email at google.com
Sebastian RiedelProfessor @ University College London, Researcher @ Facebook AI ResearchVerified email at cs.ucl.ac.uk
Aron CulottaAssociate Professor of Computer Science, Illinois Institute of TechnologyVerified email at cs.iit.edu
David MimnoAssociate Professor, Cornell UniversityVerified email at cornell.edu
Xuerui WangGoogle Inc.Verified email at google.com
Michael WickUniversity of MassachusettsVerified email at cs.umass.edu
Tom MitchellE. Fredkin University Professor of Machine Learning, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Sameer SinghUniversity of California, IrvineVerified email at uci.edu
Hanna WallachSenior Principal Researcher, Microsoft ResearchVerified email at dirichlet.net
David BelangerResearch Scientist, Google BrainVerified email at google.com
Emma StrubellAssistant Professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Luke VilnisGraduate Research Assistant, University of Massachusetts AmherstVerified email at cs.umass.edu
Gideon MannBloomberg LPVerified email at bloomberg.net
Pat VergaResearch Scientist GoogleVerified email at google.com
Jason RennieSoftware Engieer, GoogleVerified email at google.com
Alexandre PassosGoogle researchVerified email at cs.umass.edu
Arvind NeelakantanResearch Scientist, Google BrainVerified email at google.com
Chris PalProfessor, Polytechnique Montréal & Mila, Element AI, Canada CIFAR AI ChairVerified email at polymtl.ca

Andrew McCallum

Distinguished Professor of Computer Science, University of Massachusetts Amherst

Verified email at cs.umass.edu - Homepage

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


Title	Cited by	Year
Conditional random fields: Probabilistic models for segmenting and labeling sequence data J Lafferty, A McCallum, FCN Pereira	13195	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	4311	1998
Text classification from labeled and unlabeled documents using EM K Nigam, AK McCallum, S Thrun, T Mitchell Machine learning 39 (2-3), 103-134, 2000	3500	2000
Mallet: A machine learning for language toolkit AK McCallum http://mallet. cs. umass. edu, 2002	2281	2002
An introduction to conditional random fields C Sutton, A McCallum Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012	1930	2012
An introduction to conditional random fields C Sutton, A McCallum Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012	1927	2012
Maximum Entropy Markov Models for Information Extraction and Segmentation. A McCallum, D Freitag, FCN Pereira Icml 17 (2000), 591-598, 2000	1677	2000
Introduction to statistical relational learning D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ... MIT press, 2007	1565	2007
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	1412	2006
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	1266	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	1201	2003
Toward optimal active learning through monte carlo estimation of error reduction N Roy, A McCallum ICML, Williamstown, 441-448, 2001	1160	2001
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	1107	1999
Distributional clustering of words for text classification LD Baker, AK McCallum Proceedings of the 21st annual international ACM SIGIR conference on …, 1998	1001	1998
Employing EM and pool-based active learning for text classification AK McCallumzy, K Nigamy Proc. International Conference on Machine Learning (ICML), 359-367, 1998	976	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	896	1998
Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering AK McCallum CMU: Pittsburgh, PA, 1996	885*	1996
Optimizing semantic coherence in topic models D Mimno, HM Wallach, E Talley, M Leenders, A McCallum Proceedings of the conference on empirical methods in natural language …, 2011	874	2011
Automating the construction of internet portals with machine learning AK McCallum, K Nigam, J Rennie, K Seymore Information Retrieval 3 (2), 127-163, 2000	699	2000
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	681	2006

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