Conditional random fields: Probabilistic models for segmenting and labeling sequence data J Lafferty, A McCallum, FCN Pereira | 8204 | 2001 |
A comparison of event models for naive bayes text classification A McCallum, K Nigam AAAI-98 workshop on learning for text categorization 752, 41-48, 1998 | 2593 | 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 | 2474 | 2000 |
{MALLET: A Machine Learning for Language Toolkit} AK McCallum | 1396 | 2002 |
Maximum Entropy Markov Models for Information Extraction and Segmentation. A McCallum, D Freitag, FCN Pereira ICML 17, 591-598, 2000 | 1233 | 2000 |
An introduction to conditional random fields for relational learning C Sutton, A McCallum Introduction to statistical relational learning, 93-128, 2006 | 843 | 2006 |
An introduction to conditional random fields for relational learning C Sutton, A McCallum Introduction to statistical relational learning, 93-128, 2006 | 800 | 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 | 797 | 2000 |
Distributional clustering of words for text classification LD Baker, AK McCallum Proceedings of the 21st annual international ACM SIGIR conference on ..., 1998 | 781 | 1998 |
Using maximum entropy for text classification K Nigam, J Lafferty, A McCallum IJCAI-99 workshop on machine learning for information filtering 1, 61-67, 1999 | 764 | 1999 |
Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering AK McCallum CMU: Pittsburgh, PA, 1996 | 764* | 1996 |
Toward optimal active learning through monte carlo estimation of error reduction N Roy, A McCallum ICML, Williamstown, 441-448, 2001 | 759 | 2001 |
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 | 749 | 1998 |
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 | 709 | 2003 |
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 | 674 | 2006 |
Employing EM and pool-based active learning for text classification AK McCallumzy, K Nigamy Proc. International Conference on Machine Learning (ICML), 359-367, 1998 | 674 | 1998 |
Improving Text Classification by Shrinkage in a Hierarchy of Classes. A McCallum, R Rosenfeld, TM Mitchell, AY Ng ICML 98, 359-367, 1998 | 549 | 1998 |
Reinforcement learning with selective perception and hidden state AK McCallum University of Rochester, 1996 | 517 | 1996 |
Learning to construct knowledge bases from the World Wide Web M Craven, D DiPasquo, D Freitag, A McCallum, T Mitchell, K Nigam, ... Artificial intelligence 118 (1), 69-113, 2000 | 516 | 2000 |
Learning hidden Markov model structure for information extraction K Seymore, A McCallum, R Rosenfeld AAAI-99 Workshop on Machine Learning for Information Extraction, 37-42, 1999 | 488 | 1999 |
Automating the construction of internet portals with machine learning AK McCallum, K Nigam, J Rennie, K Seymore Information Retrieval 3 (2), 127-163, 2000 | 470 | 2000 |
Information extraction from research papers using conditional random fields F Peng, A McCallum Information processing & management 42 (4), 963-979, 2006 | 453 | 2006 |
Efficiently inducing features of conditional random fields A McCallum Proceedings of the Nineteenth conference on Uncertainty in Artificial ..., 2002 | 448 | 2002 |
Learning to classify text from labeled and unlabeled documents K Nigam, A McCallum, S Thrun, T Mitchell AAAI/IAAI 792, 1998 | 433 | 1998 |
Multi-label text classification with a mixture model trained by EM A McCallum AAAI’99 workshop on text learning, 1-7, 1999 | 419 | 1999 |
Information extraction with HMMs and shrinkage D Freitag, A McCallum Proceedings of the AAAI-99 workshop on machine learning for information ..., 1999 | 411 | 1999 |
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 | 397 | 2006 |
Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data C Sutton, A McCallum, K Rohanimanesh The Journal of Machine Learning Research 8, 693-723, 2007 | 378 | 2007 |
Table extraction using conditional random fields D Pinto, A McCallum, X Wei, WB Croft Proceedings of the 26th annual international ACM SIGIR conference on ..., 2003 | 365 | 2003 |
Information extraction with HMM structures learned by stochastic optimization D Freitag, A McCallum AAAI/IAAI 2000, 584-589, 2000 | 342 | 2000 |
Topic and role discovery in social networks with experiments on enron and academic email A McCallum, X Wang, A Corrada-Emmanuel Journal of Artificial Intelligence Research, 249-272, 2007 | 329 | 2007 |
Using reinforcement learning to spider the web efficiently J Rennie, A McCallum ICML 99, 335-343, 1999 | 326 | 1999 |
Chinese segmentation and new word detection using conditional random fields F Peng, F Feng, A McCallum Proceedings of the 20th international conference on Computational ..., 2004 | 320 | 2004 |
Toward conditional models of identity uncertainty with application to proper noun coreference A McCallum, B Wellner | 320 | 2003 |
Toward conditional models of identity uncertainty with application to proper noun coreference A McCallum, B Wellner | 320 | 2003 |
Disambiguating web appearances of people in a social network R Bekkerman, A McCallum Proceedings of the 14th international conference on World Wide Web, 463-470, 2005 | 297 | 2005 |
Detecting anomalies in network traffic using maximum entropy estimation Y Gu, A McCallum, D Towsley Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement, 32-32, 2005 | 293 | 2005 |
Topic and role discovery in social networks A McCallum, A Corrada-Emmanuel, X Wang Computer Science Department Faculty Publication Series, 3, 2005 | 281 | 2005 |
Collective multi-label classification N Ghamrawi, A McCallum Proceedings of the 14th ACM international conference on Information and ..., 2005 | 259 | 2005 |
Extracting social networks and contact information from email and the web A Culotta, R Bekkerman, A McCallum | 259 | 2004 |
Rethinking LDA: Why priors matter HM Wallach, DM Mimno, A McCallum Advances in neural information processing systems, 1973-1981, 2009 | 251 | 2009 |
Instance-based utile distinctions for reinforcement learning with hidden state RA McCallum ICML, 387-395, 1995 | 245 | 1995 |
Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002 J Allan, J Aslam, N Belkin, C Buckley, J Callan, B Croft, S Dumais, N Fuhr, ... ACM SIGIR Forum 37 (1), 31-47, 2003 | 244 | 2003 |
Semi-supervised clustering with user feedback D Cohn, R Caruana, A McCallum Constrained Clustering: Advances in Algorithms, Theory, and Applications 4 ..., 2003 | 240 | 2003 |
Topical n-grams: Phrase and topic discovery, with an application to information retrieval X Wang, A McCallum, X Wei Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on, 697-702, 2007 | 229 | 2007 |
A machine learning approach to building domain-specific search engines A McCallum, K Nigam, J Rennie, K Seymore IJCAI 99, 662-667, 1999 | 210 | 1999 |
Building domain-specific search engines with machine learning techniques A McCallumzy, K Nigamy, J Renniey, K Seymorey | 204 | 1999 |
Topic models conditioned on arbitrary features with dirichlet-multinomial regression D Mimno, A McCallum arXiv preprint arXiv:1206.3278, 2012 | 191 | 2012 |
An introduction to conditional random fields C Sutton, A McCallum Machine Learning 4 (4), 267-373, 2011 | 188 | 2011 |
Efficient methods for topic model inference on streaming document collections L Yao, D Mimno, A McCallum Proceedings of the 15th ACM SIGKDD international conference on Knowledge ..., 2009 | 188 | 2009 |
Overcoming incomplete perception with utile distinction memory RA McCallum Proceedings of the Tenth International Conference on Machine Learning, 190-196, 1993 | 188 | 1993 |
Polylingual topic models D Mimno, HM Wallach, J Naradowsky, DA Smith, A McCallum Proceedings of the 2009 Conference on Empirical Methods in Natural Language ..., 2009 | 181 | 2009 |
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 | 178 | 2011 |
Classification with hybrid generative/discriminative models R Raina, Y Shen, A Mccallum, AY Ng Advances in neural information processing systems, None, 2003 | 178 | 2003 |
Automatic categorization of email into folders: Benchmark experiments on Enron and SRI corpora R Bekkerman | 177 | 2004 |
Learning from labeled features using generalized expectation criteria G Druck, G Mann, A McCallum Proceedings of the 31st annual international ACM SIGIR conference on ..., 2008 | 176 | 2008 |
Information extraction: Distilling structured data from unstructured text A McCallum Queue 3 (9), 48-57, 2005 | 173 | 2005 |
Modeling relations and their mentions without labeled text S Riedel, L Yao, A McCallum Machine Learning and Knowledge Discovery in Databases, 148-163, 2010 | 171 | 2010 |
Learning to use selective attention and short-term memory in sequential tasks AK McCallum From animals to animats 4: proceedings of the fourth international ..., 1996 | 171 | 1996 |
Expertise modeling for matching papers with reviewers D Mimno, A McCallum Proceedings of the 13th ACM SIGKDD international conference on Knowledge ..., 2007 | 157 | 2007 |
Factorie: Probabilistic programming via imperatively defined factor graphs A McCallum, K Schultz, S Singh Advances in Neural Information Processing Systems, 1249-1257, 2009 | 156 | 2009 |
Interactive information extraction with constrained conditional random fields T Kristjansson, A Culotta, P Viola, A McCallum AAAI 4, 412-418, 2004 | 151 | 2004 |
Piecewise training for undirected models C Sutton, A McCallum arXiv preprint arXiv:1207.1409, 2012 | 149 | 2012 |
First-order probabilistic models for coreference resolution A Culotta, M Wick, R Hall, A McCallum | 147 | 2006 |
An integrated, conditional model of information extraction and coreference with application to citation matching B Wellner, A McCallum, F Peng, M Hay Proceedings of the 20th conference on Uncertainty in artificial intelligence ..., 2004 | 146 | 2004 |
Collective segmentation and labeling of distant entities in information extraction C Sutton, A McCallum MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE, 2004 | 134 | 2004 |
Text classification by bootstrapping with keywords, EM and shrinkage A McCallumzy, K Nigamy | 131 | 1999 |
Simple, robust, scalable semi-supervised learning via expectation regularization GS Mann, A McCallum Proceedings of the 24th international conference on Machine learning, 593-600, 2007 | 127 | 2007 |
Mixtures of hierarchical topics with pachinko allocation D Mimno, W Li, A McCallum Proceedings of the 24th international conference on Machine learning, 633-640, 2007 | 124 | 2007 |
Bootstrapping for text learning tasks R Jones, A McCallum, K Nigam, E Riloff IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications 1 (7), 1999 | 122 | 1999 |
Integrating probabilistic extraction models and data mining to discover relations and patterns in text A Culotta, A McCallum, J Betz Proceedings of the main conference on Human Language Technology Conference ..., 2006 | 120 | 2006 |
Reducing labeling effort for structured prediction tasks A Culotta, A McCallum AAAI, 746-751, 2005 | 119 | 2005 |
Group and topic discovery from relations and text X Wang, N Mohanty, A McCallum Proceedings of the 3rd international workshop on Link discovery, 28-35, 2005 | 116 | 2005 |
A conditional random field for discriminatively-trained finite-state string edit distance A McCallum, K Bellare, F Pereira arXiv preprint arXiv:1207.1406, 2012 | 109 | 2012 |
Confidence estimation for information extraction A Culotta, A McCallum Proceedings of HLT-NAACL 2004: Short Papers, 109-112, 2004 | 108 | 2004 |
Multi-way distributional clustering via pairwise interactions R Bekkerman, R El-Yaniv, A McCallum Proceedings of the 22nd international conference on Machine learning, 41-48, 2005 | 102 | 2005 |
Relation extraction with matrix factorization and universal schemas S Riedel, L Yao, A McCallum, BM Marlin | 99 | 2013 |
The author-recipient-topic model for topic and role discovery in social networks: Experiments with enron and academic email A McCallum, A Corrada-Emmanuel, X Wang | 99 | 2005 |
Learning to create customized authority lists H Chang, D Cohn, AK McCallum ICML, 127-134, 2000 | 98 | 2000 |
Generalized expectation criteria for semi-supervised learning of conditional random fields GS Mann, A McCallum | 95 | 2008 |
Rapid development of Hindi named entity recognition using conditional random fields and feature induction W Li, A McCallum ACM Transactions on Asian Language Information Processing (TALIP) 2 (3), 290-294, 2003 | 92 | 2003 |
Multi-conditional learning: Generative/discriminative training for clustering and classification A McCallum, C Pal, G Druck, X Wang PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE 21 (1), 433, 2006 | 90 | 2006 |
Hidden state and reinforcement learning with instance-based state identification RA McCallum Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 26 ..., 1996 | 83 | 1996 |
Generalized expectation criteria for semi-supervised learning with weakly labeled data GS Mann, A McCallum The Journal of Machine Learning Research 11, 955-984, 2010 | 81 | 2010 |
Bibliometric impact measures leveraging topic analysis GS Mann, D Mimno, A McCallum Digital Libraries, 2006. JCDL'06. Proceedings of the 6th ACM/IEEE-CS Joint ..., 2006 | 81 | 2006 |
Semi-supervised text classification using EM K Nigam, A McCallum, T Mitchell Semi-Supervised Learning, 33-56, 2006 | 81 | 2006 |
Piecewise pseudolikelihood for efficient training of conditional random fields C Sutton, A McCallum Proceedings of the 24th international conference on Machine learning, 863-870, 2007 | 77 | 2007 |
Instance-based state identification for reinforcement learning RA McCallum, G Tesauro, D Touretzky, T Leen Advances in Neural Information Processing Systems, 377-384, 1995 | 77 | 1995 |
Joint deduplication of multiple record types in relational data A Culotta, A McCallum Proceedings of the 14th ACM international conference on Information and ..., 2005 | 76 | 2005 |
Large-scale cross-document coreference using distributed inference and hierarchical models S Singh, A Subramanya, F Pereira, A McCallum Proceedings of the 49th Annual Meeting of the Association for Computational ..., 2011 | 74 | 2011 |
Active learning by labeling features G Druck, B Settles, A McCallum Proceedings of the 2009 Conference on Empirical Methods in Natural Language ..., 2009 | 74 | 2009 |
Organizing the OCA: learning faceted subjects from a library of digital books D Mimno, A McCallum Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries ..., 2007 | 73 | 2007 |
Nonparametric bayes pachinko allocation W Li, D Blei, A McCallum arXiv preprint arXiv:1206.5270, 2012 | 71 | 2012 |
Structured relation discovery using generative models L Yao, A Haghighi, S Riedel, A McCallum Proceedings of the Conference on Empirical Methods in Natural Language ..., 2011 | 69 | 2011 |
Collective cross-document relation extraction without labelled data L Yao, S Riedel, A McCallum Proceedings of the 2010 Conference on Empirical Methods in Natural Language ..., 2010 | 69 | 2010 |
Author disambiguation using error-driven machine learning with a ranking loss function A Culotta, P Kanani, R Hall, M Wick, A McCallum Sixth International Workshop on Information Integration on the Web (IIWeb-07 ..., 2007 | 65 | 2007 |
Improving Author Coreference by Resource-Bounded Information Gathering from the Web. PH Kanani, A McCallum, C Pal IJCAI, 429-434, 2007 | 65 | 2007 |
A note on the unification of information extraction and data mining using conditional-probability, relational models A McCallum, D Jensen | 65 | 2003 |
Object consolidation by graph partitioning with a conditionally-trained distance metric A McCallum, B Wellner KDD Workshop on Data Cleaning, Record Linkage and Object Consolidation, 2003 | 63 | 2003 |
Semi-supervised classification with hybrid generative/discriminative methods G Druck, C Pal, A McCallum, X Zhu Proceedings of the 13th ACM SIGKDD international conference on Knowledge ..., 2007 | 61 | 2007 |