| Conditional random fields: Probabilistic models for segmenting and labeling sequence data J Lafferty, A McCallum, F Pereira Icml 1 (2), 3, 2001 | 19237 | 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 | 6014 | 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 | 4405 | 2020 |
| Text classification from labeled and unlabeled documents using EM K Nigam, AK McCallum, S Thrun, T Mitchell Machine learning 39, 103-134, 2000 | 4382 | 2000 |
| Mallet: A machine learning for languagetoolkit AK McCallum http://mallet. cs. umass. edu, 2002 | 3265 | 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 | 2741 | 2011 |
| Maximum entropy Markov models for information extraction and segmentation. A McCallum, D Freitag, FCN Pereira Icml 17 (2000), 591-598, 2000 | 2165 | 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 | 1961 | 2006 |
| Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons A McCallum, W Li | 1748 | 2003 |
| Toward optimal active learning through monte carlo estimation of error reduction N Roy, A McCallum Icml, williamstown 2 (441-448), 4, 2001 | 1742 | 2001 |
| Automating the construction of internet portals with machine learning AK McCallum, K Nigam, J Rennie, K Seymore Information Retrieval 3, 127-163, 2000 | 1738 | 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 | 1652 | 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 | 1625 | 2000 |
| An introduction to conditional random fields C Sutton, A McCallum Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012 | 1603 | 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 | 1434 | 1999 |
| An introduction to conditional random fields for relational learning C Sutton Introduction to statistical relational learning, 2007 | 1348 | 2007 |
| Employing EM and Pool-Based Active Learning for Text Classification. AK McCallum, K Nigam ICML 98, 350-358, 1998 | 1312 | 1998 |
| Distributional clustering of words for text classification LD Baker, AK McCallum Proceedings of the 21st annual international ACM SIGIR conference on …, 1998 | 1137 | 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 | 1097 | 1998 |
| Rethinking LDA: Why priors matter H Wallach, D Mimno, A McCallum Advances in neural information processing systems 22, 2009 | 1032 | 2009 |
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