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Note: Links lead to the DBLP on the Web. Andrew McCallum Fuchun Peng , Andrew McCallum: Information extraction from research papers using conditional random fields. Inf. Process. Manage. 42 (4): 963-979 (2006) Aron Culotta , Andrew McCallum: Reducing Labeling Effort for Structured Prediction Tasks. AAAI 2005 : 746-751 Wei Li , Andrew McCallum: Semi-Supervised Sequence Modeling with Syntactic Topic Models. AAAI 2005 : 813-818 Nadia Ghamrawi , Andrew McCallum: Collective multi-label classification. CIKM 2005 : 195-200 Aron Culotta , Andrew McCallum: Joint deduplication of multiple record types in relational data. CIKM 2005 : 257-258 Ron Bekkerman , Ran El-Yaniv , Andrew McCallum: Multi-way distributional clustering via pairwise interactions. ICML 2005 : 41-48 Andrew McCallum, Andrés Corrada-Emmanuel , Xuerui Wang : Topic and Role Discovery in Social Networks. IJCAI 2005 : 786-791 Xuerui Wang , Natasha Mohanty , Andrew McCallum: Group and Topic Discovery from Relations and Their Attributes. NIPS 2005 Ron Bekkerman , Andrew McCallum: Disambiguating Web appearances of people in a social network. WWW 2005 : 463-470 Andrew McCallum: Information extraction: distilling structured data from unstructured text. ACM Queue 3 (9): 48-57 (2005) Trausti T. Kristjansson , Aron Culotta , Paul A. Viola , Andrew McCallum: Interactive Information Extraction with Constrained Conditional Random Fields. AAAI 2004 : 412-418 Aron Culotta , Ron Bekkerman , Andrew McCallum: Extracting social networks and contact information from email and the Web. CEAS 2004 Fuchun Peng , Andrew McCallum: Accurate Information Extraction from Research Papers using Conditional Random Fields. HLT-NAACL 2004 : 329-336 Charles A. Sutton , Khashayar Rohanimanesh , Andrew McCallum: Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. ICML 2004 Andrew McCallum, Ben Wellner : Conditional Models of Identity Uncertainty with Application to Noun Coreference. NIPS 2004 David Pinto , Andrew McCallum, Xing Wei , W. Bruce Croft : Table Extraction Using Conditional Random Fields. DG.O 2003 Andrew McCallum, Ben Wellner : Toward Conditional Models of Identity Uncertainty with Application to Proper Noun Coreference. IIWeb 2003 : 79-84 Rajat Raina , Yirong Shen , Andrew Y. Ng , Andrew McCallum: Classification with Hybrid Generative/Discriminative Models. NIPS 2003 David Pinto , Andrew McCallum, Xing Wei , W. Bruce Croft : Table extraction using conditional random fields. SIGIR 2003 : 235-242 Andrew McCallum: Efficiently Inducing Features of Conditional Random Fields. UAI 2003 : 403-410 Wei Li , Andrew McCallum: Rapid development of Hindi named entity recognition using conditional random fields and feature induction. ACM Trans. Asian Lang. Inf. Process. 2 (3): 290-294 (2003) James Allan , Jay Aslam , Nicholas J. Belkin , Chris Buckley , James P. Callan , W. Bruce Croft , Susan T. Dumais , Norbert Fuhr , Donna Harman , David J. Harper , Djoerd Hiemstra , Thomas Hofmann , Eduard H. Hovy , Wessel Kraaij , John D. Lafferty , Victor Lavrenko , David D. Lewis , Liz Liddy , R. Manmatha , Andrew McCallum, Jay M. Ponte , John M. Prager , Dragomir R. Radev , Philip Resnik , Stephen E. Robertson , Ronald Rosenfeld , Salim Roukos , Mark Sanderson , Rich Schwartz , Amit Singhal , Alan F. Smeaton , Howard R. Turtle , Ellen M. Voorhees , Ralph M. Weischedel , Jinxi Xu , ChengXiang Zhai : 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. SIGIR Forum 37 (1): 31-47 (2003) David M. Blei , J. Andrew Bagnell , Andrew McCallum: Learning with Scope, with Application to Information Extraction and Classification. UAI 2002 : 53-60 John D. Lafferty , Andrew McCallum, Fernando C. N. Pereira : Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. ICML 2001 : 282-289 Nicholas Roy , Andrew McCallum: Toward Optimal Active Learning through Sampling Estimation of Error Reduction. ICML 2001 : 441-448 Dayne Freitag , Andrew McCallum: Information Extraction with HMM Structures Learned by Stochastic Optimization. AAAI/IAAI 2000 : 584-589 Huan Chang , David Cohn , Andrew McCallum: Learning to Create Customized Authority Lists. ICML 2000 : 127-134 Andrew McCallum, Dayne Freitag , Fernando C. N. Pereira : Maximum Entropy Markov Models for Information Extraction and Segmentation. ICML 2000 : 591-598 Andrew McCallum, Kamal Nigam , Lyle H. Ungar : Efficient clustering of high-dimensional data sets with application to reference matching. KDD 2000 : 169-178 Mark Craven , Dan DiPasquo , Dayne Freitag , Andrew McCallum, Tom M. Mitchell , Kamal Nigam , Seán Slattery : Learning to construct knowledge bases from the World Wide Web. Artif. Intell. 118 (1-2): 69-113 (2000) William W. Cohen , Andrew McCallum, Dallan Quass : Learning to Understand the Web. IEEE Data Eng. Bull. 23 (3): 17-24 (2000) Andrew McCallum, Kamal Nigam , Jason Rennie , Kristie Seymore : Automating the Construction of Internet Portals with Machine Learning. Inf. Retr. 3 (2): 127-163 (2000) Kamal Nigam , Andrew McCallum, Sebastian Thrun , Tom M. Mitchell : Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning 39 (2/3): 103-134 (2000) Jason Rennie , Andrew McCallum: Using Reinforcement Learning to Spider the Web Efficiently. ICML 1999 : 335-343 Andrew McCallum, Kamal Nigam , Jason Rennie , Kristie Seymore : A Machine Learning Approach to Building Domain-Specific Search Engines. IJCAI 1999 : 662-667 Mark Craven , Dan DiPasquo , Dayne Freitag , Andrew McCallum, Tom M. Mitchell , Kamal Nigam , Seán Slattery : Learning to Extract Symbolic Knowledge from the World Wide Web. AAAI/IAAI 1998 : 509-516 Kamal Nigam , Andrew McCallum, Sebastian Thrun , Tom M. Mitchell : Learning to Classify Text from Labeled and Unlabeled Documents. AAAI/IAAI 1998 : 792-799 Andrew McCallum, Kamal Nigam : Employing EM and Pool-Based Active Learning for Text Classification. ICML 1998 : 350-358 Andrew McCallum, Ronald Rosenfeld , Tom M. Mitchell , Andrew Y. Ng : Improving Text Classification by Shrinkage in a Hierarchy of Classes. ICML 1998 : 359-367 L. Douglas Baker , Andrew McCallum: Distributional Clustering of Words for Text Classification. SIGIR 1998 : 96-103 Andrew McCallum: Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State. ICML 1995 : 387-395 Andrew McCallum: Instance-Based State Identification for Reinforcement Learning. NIPS 1994 : 377-384 Andrew McCallum: Overcoming Incomplete Perception with Util Distinction Memory. ICML 1993 : 190-196 Andrew McCallum: Using Transitional Proximity for Faster Reinforcement Learning. ML 1992 : 316-321 Andrew McCallum, Kent A. Spackman : Using Genetic Algorithms to Learn Disjunctive Rules from Examples. 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