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Note: Links lead to the DBLP on the Web. Raymond J. Mooney 58 Cynthia A. Thompson , Raymond J. Mooney: Acquiring Word-Meaning Mappings for Natural Language Interfaces. Journal of Artificial Intelligence Research 18 : 1-44 (2003) 57 Prem Melville , Raymond J. Mooney, Ramadass Nagarajan : Content-Boosted Collaborative Filtering for Improved Recommendations. AAAI/IAAI 2002 : 187-192 56 Un Yong Nahm , Raymond J. Mooney: Mining soft-matching association rules. CIKM 2002 : 681-683 55 Sugato Basu , Arindam Banerjee , Raymond J. Mooney: Semi-supervised Clustering by Seeding. ICML 2002 : 27-34 54 Lappoon R. Tang , Raymond J. Mooney: Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing. ECML 2001 : 466-477 53 Un Yong Nahm , Raymond J. Mooney: Mining Soft-Matching Rules from Textual Data. IJCAI 2001 : 979-986 52 Sugato Basu , Raymond J. Mooney, Krupakar V. Pasupuleti , Joydeep Ghosh : Evaluating the novelty of text-mined rules using lexical knowledge. KDD 2001 : 233-238 51 Un Yong Nahm , Raymond J. Mooney: A Mutually Beneficial Integration of Data Mining and Information Extraction. AAAI/IAAI 2000 : 627-632 50 Raymond J. Mooney, Loriene Roy : Content-based book recommending using learning for text categorization. ACM DL 2000 : 195-204 49 Mary Elaine Califf , Raymond J. Mooney: Relational Learning of Pattern-Match Rules for Information Extraction. AAAI/IAAI 1999 : 328-334 48 Cynthia A. Thompson , Raymond J. Mooney: Automatic Construction of Semantic Lexicons for Learning Natural Language Interfaces. AAAI/IAAI 1999 : 487-493 47 Cynthia A. Thompson , Mary Elaine Califf , Raymond J. Mooney: Active Learning for Natural Language Parsing and Information Extraction. ICML 1999 : 406-414 46 Raymond J. Mooney: Learning for Semantic Interpretation: Scaling Up without Dumbing Down. Learning Language in Logic 1999 : 57-66 45 Claire Cardie , Raymond J. Mooney: Guest Editors' Introduction: Machine Learning and Natural Language. Machine Learning 34 (1-3): 5-9 (1999) 44 Sowmya Ramachandran , Raymond J. Mooney: Theory Refinement of Bayesian Networks with Hidden Variables. ICML 1998 : 454-462 43 Mary Elaine Califf , Raymond J. Mooney: Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming. New Generation Computing 16 (3): 263-281 (1998) 42 Ulf Hermjakob , Raymond J. Mooney: Learning Parse and Translation Decisions from Examples with Rich Context. ACL 1997 : 482-489 41 Tara A. Estlin , Raymond J. Mooney: Learning to Improve both Efficiency and Quality of Planning. IJCAI 1997 : 1227-1233 40 Eric Brill , Raymond J. Mooney: An Overview of Empirical Natural Language Processing. AI Magazine 18 (4): 13-24 (1997) 39 Paul T. Baffes , Raymond J. Mooney: A Novel Application of Theory Refinement to Student Modeling. AAAI/IAAI, Vol. 1 1996 : 403-408 38 Tara A. Estlin , Raymond J. Mooney: Multi-Strategy Learning of Search Control for Partial-Order Planning. AAAI/IAAI, Vol. 1 1996 : 843-848 37 John M. Zelle , Raymond J. Mooney: Learning to Parse Database Queries Using Inductive Logic Programming. AAAI/IAAI, Vol. 2 1996 : 1050-1055 36 Siddarth Subramanian , Raymond J. Mooney: Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using Behavioral Modes. AAAI/IAAI, Vol. 2 1996 : 965-970 35 Raymond J. Mooney: Inductive Logic Programming for Natural Language Processing. Inductive Logic Programming Workshop 1996 : 3-22 34 John M. Zelle , Raymond J. Mooney: Comparative results on using inductive logic programming for corpus-based parser construction. Learning for Natural Language Processing 1995 : 355-369 33 Raymond J. Mooney, Mary Elaine Califf : Learning the past tense of English verbs using inductive logic programming. Learning for Natural Language Processing 1995 : 370-384 32 Raymond J. Mooney, Mary Elaine Califf : Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs. JAIR 3 : 1-24 (1995) 31 Raymond J. Mooney: Encouraging Experimental Results on Learning CNF. Machine Learning 19 (1): 79-92 (1995) 30 Bradley L. Richards , Raymond J. Mooney: Automated Refinement of First-Order Horn-Clause Domain Theories. Machine Learning 19 (2): 95-131 (1995) 29 Cynthia A. Thompson , Raymond J. Mooney: Inductive Learning For Abductive Diagnosis. AAAI 1994 : 664-669 28 John M. Zelle , Raymond J. Mooney: Inducing Deterministic Prolog Parsers from Treebanks: A Machine Learning Approach. AAAI 1994 : 748-753 27 J. Jeffrey Mahoney , Raymond J. Mooney: Comparing Methods for Refining Certainty-Factor Rule-Bases. ICML 1994 : 173-180 26 John M. Zelle , Raymond J. Mooney, Joshua B. Konvisser : Combining Top-down and Bottom-up Techniques in Inductive Logic Programming. ICML 1994 : 343-351 25 Dirk Ourston , Raymond J. Mooney: Theory Refinement Combining Analytical and Empirical Methods. Artificial Intelligence 66 (2): 273-309 (1994) 24 Raymond J. Mooney, John M. Zelle : Integrating ILP and EBL. SIGART Bulletin 5 (1): 12-21 (1994) 23 John M. Zelle , Raymond J. Mooney: Learning Semantic Grammars with Constructive Inductive Logic Programming. AAAI 1993 : 817-822 22 John M. Zelle , Raymond J. Mooney: Combining FOIL and EBG to Speed-up Logic Programs. IJCAI 1993 : 1106-1113 21 Paul T. Baffes , Raymond J. Mooney: Symbolic Revision of Theories with M-of-N Rules. IJCAI 1993 : 1135-1142 20 Raymond J. Mooney: Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning. Machine Learning 10 : 79-110 (1993) 19 Bradley L. Richards , Raymond J. Mooney: Learning Relations by Pathfinding. AAAI 1992 : 50-55 18 Hwee Tou Ng , Raymond J. Mooney: Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation. KR 1992 : 499-508 17 J. Jeffrey Mahoney , Raymond J. Mooney: Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases. NIPS 1992 : 107-114 16 Hwee Tou Ng , Raymond J. Mooney: An Efficient First-Order Horn-Clause Abduction System Based on the ATMS. AAAI 1991 : 494-499 15 Raymond J. Mooney, Dirk Ourston : Constructive Induction in Theory Refinement. ML 1991 : 178-182 14 Bradley L. Richards , Raymond J. Mooney: First-Order Theory Revision. ML 1991 : 447-451 13 Dirk Ourston , Raymond J. Mooney: Improving Shared Rules in Multiple Category Domain Theories. ML 1991 : 534-538 12 Jude W. Shavlik , Raymond J. Mooney, Geoffrey G. Towell : Symbolic and Neural Learning Algorithms: An Experimental Comparison. Machine Learning 6 : 111-143 (1991) 11 Hwee Tou Ng , Raymond J. Mooney: On the Role of Coherence in Abductive Explanation. AAAI 1990 : 337-342 10 Dirk Ourston , Raymond J. Mooney: Changing the Rules: A Comprehensive Approach to Theory Refinement. AAAI 1990 : 815-820 9 Raymond J. Mooney: Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition. Cognitive Science 14 (4): 483-509 (1990) 8 Raymond J. Mooney: The Effect of Rule Use on the Utility of Explanation-Based Learning. IJCAI 1989 : 725-730 7 Raymond J. Mooney, Jude W. Shavlik , Geoffrey G. Towell , Alan Gove : An Experimental Comparison of Symbolic and Connectionist Learning Algorithms. IJCAI 1989 : 775-780 6 Douglas H. Fisher , Kathleen B. McKusick , Raymond J. Mooney, Jude W. Shavlik , Geoffrey G. Towell : Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems. ML 1989 : 169-173 5 Raymond J. Mooney, Dirk Ourston : Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects. ML 1989 : 5-7 4 Raymond J. Mooney: Generalizing the Order of Operators in Macro-Operators. ML 1988 : 270-283 3 Raymond J. Mooney, Scott Bennett : A Domain Independent Explanation-Based Generalizer. AAAI 1986 : 551-555 2 Gerald DeJong , Raymond J. Mooney: Explanation-Based Learning: An Alternative View. Machine Learning 1 (2): 145-176 (1986) 1 Raymond J. Mooney, Gerald DeJong : Learning Schemata for Natural Language Processing. IJCAI 1985 : 681-687  DiSC'03 © 2003 Association for Computing Machinery |
