Andrew McCallum
TitleCited byYear
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
J Lafferty, A McCallum, FCN Pereira
127532001
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
41251998
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
34122000
Mallet: A machine learning for language toolkit
AK McCallum
http://mallet. cs. umass. edu, 2002
22092002
An introduction to conditional random fields
C Sutton, A McCallum
Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012
18082012
An introduction to conditional random fields
C Sutton, A McCallum
Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012
18082012
Maximum Entropy Markov Models for Information Extraction and Segmentation.
A McCallum, D Freitag, FCN Pereira
Icml 17 (2000), 591-598, 2000
16302000
Introduction to statistical relational learning
D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ...
MIT press, 2007
15072007
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
13472006
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
12242000
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
11482003
Toward optimal active learning through monte carlo estimation of error reduction
N Roy, A McCallum
ICML, Williamstown, 441-448, 2001
11222001
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
10631999
Distributional clustering of words for text classification
LD Bakeryz, AK McCallumyz
Proceedings of SIGIR, 1998
9631998
Employing EM and pool-based active learning for text classification
AK McCallumzy, K Nigamy
Proc. International Conference on Machine Learning (ICML), 359-367, 1998
9321998
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
8861998
Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering
AK McCallum
CMU: Pittsburgh, PA, 1996
864*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
8042011
Automating the construction of internet portals with machine learning
AK McCallum, K Nigam, J Rennie, K Seymore
Information Retrieval 3 (2), 127-163, 2000
6642000
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
6542006
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Articles 1–20