Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: All Publications

  1. Probabilistic Soft Logic for Semantic Textual Similarity
    [Details] [PDF]
    Islam Beltagy and Katrin Erk and Raymond J. Mooney
    To Appear In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL-14), Baltimore, MD, 2014.
  2. Plan Recognition Using Statistical Relational Models
    [Details] [PDF]
    Sindhu Raghavan and Parag Singla and Raymond J. Mooney
    In Sukthankar, G. and Geib, C. and Bui, H.H. and Pynadath, D. and Goldman, R.P., editors, Plan, Activity, and Intent Recognition: Theory and Practice, 57--85, Burlington, MA, 2014. Morgan Kaufmann.
  3. Active Multitask Learning Using Both Latent and Supervised Shared Topics
    [Details] [PDF]
    Ayan Acharya and Raymond J. Mooney and Joydeep Ghosh
    To Appear In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM14), Philadelphia, Pennsylvania, April 2014.
  4. Statistical Script Learning with Multi-Argument Events
    [Details] [PDF]
    Karl Pichotta and Raymond J. Mooney
    To Appear In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014), Gothenburg, Sweden, April 2014.
  5. University of Texas at Austin KBP 2013 Slot Filling System: Bayesian Logic Programs for Textual Inference
    [Details] [PDF]
    Yinon Bentor and Amelia Harrison and Shruti Bhosale and Raymond Mooney
    In Proceedings of the Sixth Text Analysis Conference (TAC 2013), 2013.
  6. YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-shot Recognition
    [Details] [PDF] [Poster]
    Sergio Guadarrama, Niveda Krishnamoorthy, Girish Malkarnenkar, Subhashini Venugopalan, Raymond Mooney, Trevor Darrell, Kate Saenko
    In Proceedings of the 14th International Conference on Computer Vision (ICCV-2013), 2712--2719, Sydney, Australia, December 2013.
  7. A Multimodal LDA Model Integrating Textual, Cognitive and Visual Modalities
    [Details] [PDF]
    Stephen Roller and Sabine Schulte im Walde
    In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), 1146--1157, Seattle, WA, October 2013.
  8. Identifying Phrasal Verbs Using Many Bilingual Corpora
    [Details] [PDF] [Poster]
    Karl Pichotta and John DeNero
    In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), 636--646, Seattle, WA, October 2013.
  9. Detecting Promotional Content in Wikipedia
    [Details] [PDF] [Slides]
    Shruti Bhosale and Heath Vinicombe and Raymond J. Mooney
    In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), 1851--1857, Seattle, WA, October 2013.
  10. Grounded Language Learning Models for Ambiguous Supervision
    [Details] [PDF] [Slides]
    Joo Hyun Kim
    PhD Thesis, Department of Computer Science, University of Texas at Austin, December 2013.
  11. Generating Natural-Language Video Descriptions Using Text-Mined Knowledge
    [Details] [PDF] [Slides]
    Niveda Krishnamoorthy, Girish Malkarnenkar, Raymond J. Mooney, Kate Saenko, Sergio Guadarrama
    In Proceedings of the NAACL HLT Workshop on Vision and Language (WVL '13), 10--19, Atlanta, Georgia, July 2013.
  12. Using Both Latent and Supervised Shared Topics for Multitask Learning
    [Details] [PDF] [Slides]
    Ayan Acharya, Aditya Rawal, Raymond J. Mooney, Eduardo R. Hruschka
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 369--384, Prague, Czech Republic, September 2013.
  13. Real-World Semi-Supervised Learning of POS-Taggers for Low-Resource Languages
    [Details] [PDF]
    Dan Garrette and Jason Mielens and Jason Baldridge
    To Appear In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013), 583--592, Sofia, Bulgaria, August 2013.
  14. Online Inference-Rule Learning from Natural-Language Extractions
    [Details] [PDF] [Poster]
    Sindhu Raghavan and Raymond J. Mooney
    In Proceedings of the 3rd Statistical Relational AI (StaRAI-13) workshop at AAAI '13, July 2013.
  15. Adapting Discriminative Reranking to Grounded Language Learning
    [Details] [PDF] [Slides]
    Joohyun Kim and Raymond J. Mooney
    In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013), 218--227, Sofia, Bulgaria, August 2013.
  16. Montague Meets Markov: Deep Semantics with Probabilistic Logical Form
    [Details] [PDF] [Slides]
    Islam Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond Mooney
    In Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*Sem-2013), 11--21, Atlanta, GA, June 2013.
  17. A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics
    [Details] [PDF]
    Dan Garrette, Katrin Erk, Raymond J. Mooney
    In Harry Bunt, Johan Bos, and Stephen Pulman, editors, Computing Meaning, 27--48, Berlin, 2013. Springer.
  18. Learning a Part-of-Speech Tagger from Two Hours of Annotation
    [Details] [PDF] [Slides] [Video]
    Dan Garrette, Jason Baldridge
    In Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-13), 138--147, Atlanta, GA, June 2013.
  19. Generating Natural-Language Video Descriptions Using Text-Mined Knowledge
    [Details] [PDF] [Slides]
    Niveda Krishnamoorthy, Girish Malkarnenkar, Raymond J. Mooney, Kate Saenko, Sergio Guadarrama
    In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI-2013), 541--547, July 2013.
  20. Review Quality Aware Collaborative Filtering
    [Details] [PDF]
    Sindhu Raghavan and Suriya Ganasekar and Joydeep Ghosh
    In Sixth ACM Conference on Recommender Systems (RecSys 2012), 123--130, September 2012.
  21. Bayesian Logic Programs for Plan Recognition and Machine Reading
    [Details] [PDF] [Slides]
    Sindhu Raghavan
    PhD Thesis, Department of Computer Science, University of Texas at Austin, December 2012. 170.
  22. Type-Supervised Hidden Markov Models for Part-of-Speech Tagging with Incomplete Tag Dictionaries
    [Details] [PDF]
    Dan Garrette and Jason Baldridge
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012), 821--831, Jeju, Korea, July 2012.
  23. Improving Video Activity Recognition using Object Recognition and Text Mining
    [Details] [PDF] [Slides]
    Tanvi S. Motwani and Raymond J. Mooney
    In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI-2012), 600--605, August 2012.
  24. Generative Models of Grounded Language Learning with Ambiguous Supervision
    [Details] [PDF] [Slides]
    Joohyun Kim
    Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin, June 2012.
  25. Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision
    [Details] [PDF]
    Joohyun Kim and Raymond J. Mooney
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Natural Language Learning (EMNLP-CoNLL '12), 433--444, Jeju Island, Korea, July 2012.
  26. Fast Online Lexicon Learning for Grounded Language Acquisition
    [Details] [PDF] [Slides]
    David L. Chen
    In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012), 430--439, July 2012.
  27. Learning to "Read Between the Lines" using Bayesian Logic Programs
    [Details] [PDF] [Slides]
    Sindhu Raghavan and Raymond J. Mooney and Hyeonseo Ku
    In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012), 349--358, July 2012.
  28. Learning Language from Ambiguous Perceptual Context
    [Details] [PDF] [Slides]
    David L. Chen
    PhD Thesis, Department of Computer Science, University of Texas at Austin, May 2012. 196.
  29. Constraint Propagation for Efficient Inference in Markov Logic
    [Details] [PDF] [Slides]
    Tivadar Papai, Parag Singla and Henry Kautz
    In Proceedings of 17th International Conference on Principles and Practice of Constraint Programming (CP 2011), Lecture Notes in Computer Science (LNCS), 691-705, September 2011.
  30. Online Structure Learning for Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), 81-96, September 2011.
  31. Abductive Plan Recognition by Extending Bayesian Logic Programs
    [Details] [PDF] [Slides]
    Sindhu Raghavan, Raymond J. Mooney
    In Proceedings of the European Conference on Machine Learning/Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), 629-644, September 2011.
  32. Building a Persistent Workforce on Mechanical Turk for Multilingual Data Collection
    [Details] [PDF] [Slides]
    David L. Chen and William B. Dolan
    In Proceedings of The 3rd Human Computation Workshop (HCOMP 2011), August 2011.
  33. Learning to Interpret Natural Language Navigation Instructions from Observations
    [Details] [PDF] [Slides]
    David L. Chen and Raymond J. Mooney
    In Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-2011), 859-865, August 2011.
  34. Abductive Markov Logic for Plan Recognition
    [Details] [PDF] [Slides]
    Parag Singla and Raymond J. Mooney
    In Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-2011), 1069-1075, August 2011.
  35. Cross-Cutting Models of Lexical Semantics
    [Details] [PDF] [Slides]
    Joseph Reisinger and Raymond Mooney
    In Proceedings of The Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), 1405-1415, July 2011.
  36. Panning for Gold: Finding Relevant Semantic Content for Grounded Language Learning
    [Details] [PDF] [Slides]
    David L. Chen and Raymond J. Mooney
    In Proceedings of Symposium on Machine Learning in Speech and Language Processing (MLSLP 2011), June 2011.
  37. Fine-Grained Class Label Markup of Search Queries
    [Details] [PDF]
    Joseph Reisinger and Marius Pasca
    In Proceedings of The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT 2011), 1200-1209, June 2011.
  38. Collecting Highly Parallel Data for Paraphrase Evaluation
    [Details] [PDF] [Slides]
    David L. Chen and William B. Dolan
    In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, 190-200, Portland, Oregon, USA, June 2011.
  39. Extending Bayesian Logic Programs for Plan Recognition and Machine Reading
    [Details] [PDF] [Slides]
    Sindhu V. Raghavan
    Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin, May 2011.
  40. Improving the Accuracy and Scalability of Discriminative Learning Methods for Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh
    PhD Thesis, Department of Computer Science, University of Texas at Austin, May 2011.
    159 pages.
  41. Online Max-Margin Weight Learning for Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the Eleventh SIAM International Conference on Data Mining (SDM11), 642--651, Mesa, Arizona, USA, April 2011.
  42. Implementing Weighted Abduction in Markov Logic
    [Details] [PDF]
    James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate, Raymond J. Mooney
    In Proceedings of the International Conference on Computational Semantics, 55--64, Oxford, England, January 2011.
  43. Integrating Logical Representations with Probabilistic Information using Markov Logic
    [Details] [PDF] [Slides]
    Dan Garrette, Katrin Erk, Raymond Mooney
    In Proceedings of the International Conference on Computational Semantics, 105--114, Oxford, England, January 2011.
  44. A Mixture Model with Sharing for Lexical Semantics
    [Details] [PDF] [Slides]
    Joseph Reisinger and Raymond J. Mooney
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2010), 1173--1182, MIT, Massachusetts, USA, October 9--11 2010.
  45. Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision
    [Details] [PDF]
    Joohyun Kim and Raymond J. Mooney
    In Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), 543--551, Beijing, China, August 2010.
  46. Learning to Predict Readability using Diverse Linguistic Features
    [Details] [PDF] [Slides]
    Rohit J. Kate, Xiaoqiang Luo, Siddharth Patwardhan, Martin Franz, Radu Florian, Raymond J. Mooney, Salim Roukos and Chris Welty
    In 23rd International Conference on Computational Linguistics (COLING 2010), 2010.
  47. Cross-cutting Models of Distributional Lexical Semantics
    [Details] [PDF] [Slides]
    Joseph S. Reisinger
    June 2010. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  48. Spherical Topic Models
    [Details] [PDF] [Slides]
    Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond J. Mooney
    In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), 2010.
  49. Joint Entity and Relation Extraction using Card-Pyramid Parsing
    [Details] [PDF] [Slides]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the Fourteenth Conference on Computational Natural Language Learning (CoNLL-2010), 203--212, Uppsala, Sweden, July 2010.
  50. Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques
    [Details] [PDF] [Slides]
    Ruifang Ge
    PhD Thesis, Department of Computer Science, University of Texas at Austin, Austin, TX, May 2010. 165 pages.
  51. Online Max-Margin Weight Learning with Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), 32--37, Atlanta, GA, July 2010.
  52. Bayesian Abductive Logic Programs
    [Details] [PDF] [Slides]
    Sindhu Raghavan and Raymond Mooney
    In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), 82--87, Atlanta, GA, July 2010.
  53. Authorship Attribution Using Probabilistic Context-Free Grammars
    [Details] [PDF] [Slides]
    Sindhu Raghavan, Adriana Kovashka and Raymond Mooney
    In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL-2010), 38--42, 2010.
  54. Using Closed Captions as Supervision for Video Activity Recognition
    [Details] [PDF]
    Sonal Gupta, Raymond J. Mooney
    In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2010), 1083--1088, Atlanta, GA, July 2010.
  55. Multi-Prototype Vector-Space Models of Word Meaning
    [Details] [PDF] [Slides]
    Joseph Reisinger, Raymond J. Mooney
    In Proceedings of the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2010), 109-117, 2010.
  56. Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language
    [Details] [PDF]
    David L. Chen, Joohyun Kim, Raymond J. Mooney
    Journal of Artificial Intelligence Research, 37:397--435, 2010.
  57. Learning Language from Perceptual Context
    [Details] [PDF] [Slides]
    David L. Chen
    December 2009. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  58. Discriminative Learning with Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh
    October 2009. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  59. Spherical Topic Models
    [Details] [PDF]
    Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond Mooney
    In NIPS'09 workshop: Applications for Topic Models: Text and Beyond, 2009.
  60. Activity Retrieval in Closed Captioned Videos
    [Details] [PDF]
    Sonal Gupta
    Masters Thesis, Department of Computer Sciences, University of Texas at Austin, August 2009. 64 pages.
  61. Learning with Markov Logic Networks: Transfer Learning, Structure Learning, and an Application to Web Query Disambiguation
    [Details] [PDF]
    Lilyana Mihalkova
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2009. 176 pages.
  62. Max-Margin Weight Learning for Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part 1, 564--579, Bled, Slovenia, September 2009.
  63. Learning to Disambiguate Search Queries from Short Sessions
    [Details] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part 2, 111--127, Bled, Slovenia, September 2009.
  64. Max-Margin Weight Learning for Markov Logic Networks
    [Details] [PDF]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the International Workshop on Statistical Relational Learning (SRL-09), Leuven, Belgium, July 2009.
  65. Speeding up Inference In Statistical Relational Learning by Clustering Similar Query Literals
    [Details] [PDF]
    Lilyana Mihalkova and Matthew Richardson
    In Proceedings of the 19th International Conference on Inductive Logic Programming (ILP-09), Leuven, Belgium, July 2009.
  66. Learning a Compositional Semantic Parser using an Existing Syntactic Parser
    [Details] [PDF] [Slides]
    Ruifang Ge and Raymond J. Mooney
    In Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL-IJCNLP 2009), 611--619, Suntec, Singapore, August 2009.
  67. Probabilistic Abduction using Markov Logic Networks
    [Details] [PDF] [Slides]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the IJCAI-09 Workshop on Plan, Activity, and Intent Recognition (PAIR-09), Pasadena, CA, July 2009.
  68. Transfer Learning from Minimal Target Data by Mapping across Relational Domains
    [Details] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), 1163--1168, Pasadena, CA, July 2009.
  69. Using Closed Captions to Train Activity Recognizers that Improve Video Retrieval
    [Details] [PDF]
    Sonal Gupta and Raymond Mooney
    In Proceedings of the CVPR-09 Workshop on Visual and Contextual Learning from Annotated Images and Videos (VCL), Miami, FL, June 2009.
  70. Semi-supervised graph clustering: a kernel approach
    [Details] [PDF]
    Brian Kulis, Sugato Basu, Inderjit Dhillon, and Raymond Mooney
    Machine Learning Journal, 74(1):1-22, 2009.
  71. Search Query Disambiguation from Short Sessions
    [Details] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Beyond Search: Computational Intelligence for the Web Workshop at NIPS, 2008.
  72. A Dependency-based Word Subsequence Kernel
    [Details] [PDF]
    Rohit J. Kate
    In Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP-2008), 400--409, Waikiki, Honolulu, Hawaii, October 2008.
  73. Transforming Meaning Representation Grammars to Improve Semantic Parsing
    [Details] [PDF]
    Rohit J. Kate
    In Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL-2008), 33--40, Manchester, UK, August 2008.
  74. Watch, Listen & Learn: Co-training on Captioned Images and Videos
    [Details] [PDF]
    Sonal Gupta, Joohyun Kim, Kristen Grauman and Raymond Mooney
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 457--472, Antwerp Belgium, September 2008.
  75. Discriminative Structure and Parameter Learning for Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
  76. Learning to Sportscast: A Test of Grounded Language Acquisition
    [Details] [PDF] [Slides] [Video]
    David L. Chen and Raymond J. Mooney
    In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
  77. Transfer Learning by Mapping with Minimal Target Data
    [Details] [PDF]
    Lilyana Mihalkova and Raymond J. Mooney
    In Proceedings of the AAAI-08 Workshop on Transfer Learning For Complex Tasks, Chicago, IL, July 2008.
  78. Learning to Connect Language and Perception
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI), 1598--1601, Chicago, IL, July 2008. Senior Member Paper.
  79. Improving Learning of Markov Logic Networks using Transfer and Bottom-Up Induction
    [Details] [PDF]
    Lilyana Mihalkova
    Technical Report UT-AI-TR-07-341, Artificial Intelligence Lab, University of Texas at Austin, Austin, TX, May 2007.
  80. Learning for Semantic Parsing with Kernels under Various Forms of Supervision
    [Details] [PDF] [Slides]
    Rohit J. Kate
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2007. 159 pages.
  81. Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques
    [Details] [PDF]
    Yuk Wah Wong
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2007. 188 pages. Also appears as Technical Report AI07-343, Artificial Intelligence Lab, University of Texas at Austin, August 2007.
  82. Learning for Information Extraction: From Named Entity Recognition and Disambiguation To Relation Extraction
    [Details] [PDF]
    Razvan Constantin Bunescu
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2007. 150 pages. Also as Technical Report AI07-345, Artificial Intelligence Lab, University of Texas at Austin, August 2007.
  83. Learning to Extract Relations from the Web using Minimal Supervision
    [Details] [PDF]
    Razvan C. Bunescu and Raymond J. Mooney
    In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL'07), Prague, Czech Republic, June 2007.
  84. Mapping and Revising Markov Logic Networks for Transfer Learning
    [Details] [PDF]
    Lilyana Mihalkova, Tuyen N. Huynh, Raymond J. Mooney
    In Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-07), 608-614, Vancouver, BC, July 2007.
  85. Bottom-Up Learning of Markov Logic Network Structure
    [Details] [PDF]
    Lilyana Mihalkova and Raymond J. Mooney
    In Proceedings of 24th International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
  86. Multiple Instance Learning for Sparse Positive Bags
    [Details] [PDF]
    Razvan C. Bunescu and Raymond J. Mooney
    In Proceedings of the 24th Annual International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
  87. Learning Language Semantics from Ambiguous Supervision
    [Details] [PDF]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the 22nd Conference on Artificial Intelligence (AAAI-07), 895-900, Vancouver, Canada, July 2007.
  88. Learning Synchronous Grammars for Semantic Parsing with Lambda Calculus
    [Details] [PDF]
    Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-2007), Prague, Czech Republic, June 2007.
  89. Semi-Supervised Learning for Semantic Parsing using Support Vector Machines
    [Details] [PDF] [Slides]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (NAACL/HLT-2007), 81--84, Rochester, NY, April 2007.
  90. Generation by Inverting a Semantic Parser That Uses Statistical Machine Translation
    [Details] [PDF]
    Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of Human Language Technologies: The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT-07), 172-179, Rochester, NY, 2007.
  91. Learning for Semantic Parsing
    [Details] [PDF]
    Raymond J. Mooney
    In A. Gelbukh, editors, Computational Linguistics and Intelligent Text Processing: Proceedings of the 8th International Conference (CICLing 2007), 311--324, Mexico City, Mexico, February 2007. Springer: Berlin, Germany. Invited paper.
  92. Extracting Relations from Text: From Word Sequences to Dependency Paths
    [Details] [PDF]
    Razvan C. Bunescu and Raymond J. Mooney
    In A. Kao and S. Poteet, editors, Natural Language Processing and Text Mining, 29-44, Berlin, 2007. Springer Verlag.
  93. Statistical Relational Learning for Natural Language Information Extraction
    [Details] [PDF]
    Razvan Bunescu and Raymond J. Mooney
    In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning, 535-552, Cambridge, MA, 2007. MIT Press.
  94. Adaptive Blocking: Learning to Scale Up Record Linkage
    [Details] [PDF]
    Mikhail Bilenko, Beena Kamath, Raymond J. Mooney
    In Proceedings of the Sixth IEEE International Conference on Data Mining (ICDM-06), 87--96, Hong Kong, December 2006.
  95. Fast and Effective Worm Fingerprinting via Machine Learning
    [Details] [PDF]
    Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney
    In Proceedings of the 3rd IEEE International Conference on Autonomic Computing (ICAC-2006), Dublin, Ireland, June 2006. Poster Session.
  96. Learnable Similarity Functions and Their Application to Record Linkage and Clustering
    [Details] [PDF]
    Mikhail Bilenko
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2006. 136 pages.
  97. Learning Language from Perceptual Context: A Challenge Problem for AI
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the 2006 AAAI Fellows Symposium, Boston, MA, July 2006.
  98. Using String-Kernels for Learning Semantic Parsers
    [Details] [PDF] [Slides]
    Rohit J. Kate and Raymond J. Mooney
    In ACL 2006: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL, 913-920, Morristown, NJ, USA, 2006. Association for Computational Linguistics.
  99. Discriminative Reranking for Semantic Parsing
    [Details] [PDF]
    Ruifang Ge and Raymond J. Mooney
    In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL-06), Sydney, Australia, July 2006.
  100. Transfer Learning with Markov Logic Networks
    [Details] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, Pittsburgh, PA, June 2006.
  101. Integrating Co-occurrence Statistics with Information Extraction for Robust Retrieval of Protein Interactions from Medline
    [Details] [PDF]
    Razvan Bunescu, Raymond Mooney, Arun Ramani and Edward Marcotte
    In Proceedings of the HLT-NAACL Workshop on Linking Natural Language Processing and Biology (BioNLP'06), 49-56, New York, NY, June 2006.
  102. Learning for Semantic Parsing with Statistical Machine Translation
    [Details] [PDF]
    Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of Human Language Technology Conference / North American Chapter of the Association for Computational Linguistics Annual Meeting (HLT-NAACL-06), 439-446, New York City, NY, 2006.
  103. Using Encyclopedic Knowledge for Named Entity Disambiguation
    [Details] [PDF]
    Razvan Bunescu and Marius Pasca
    In Proceesings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL-06), 9-16, Trento, Italy, 2006.
  104. Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques
    [Details] [PDF]
    Ruifang Ge
    2006. Doctoral Dissertation Proposal, University of Texas at Austin" , year="2006.
  105. Fast and Effective Worm Fingerprinting via Machine Learning
    [Details] [PDF]
    Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney
    Technical Report AI-06-335, Artificial Intelligence Lab, The University of Texas at Austin, August 2006. This is a longer version of our ICAC-2006 paper.
  106. Probabilistic Semi-Supervised Clustering with Constraints
    [Details] [PDF]
    Sugato Basu, Mikhail Bilenko, Arindam Banerjee and Raymond J. Mooney
    In O. Chapelle and B. Sch{"{o}}lkopf and A. Zien, editors, Semi-Supervised Learning, Cambridge, MA, 2006. MIT Press.
  107. Subsequence Kernels for Relation Extraction
    [Details] [PDF]
    Razvan Bunescu and Raymond J. Mooney
    In Submitted to the Ninth Conference on Natural Language Learning (CoNLL-2005), Ann Arbor, MI, July 2006. Available at url{http://www.cs.utexas.edu/users/ml/publication/ie.html}.
  108. Using Active Relocation to Aid Reinforcement Learning
    [Details] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Prodeedings of the 19th International FLAIRS Conference (FLAIRS-2006), 580-585, Melbourne Beach, FL, May 2006.
  109. Creating Diverse Ensemble Classifiers to Reduce Supervision
    [Details] [PDF]
    Prem Melville
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, November 2005. 141 pages. Technical Report TR-05-49.
  110. A Kernel-based Approach to Learning Semantic Parsers
    [Details] [PDF] [Slides]
    Rohit J. Kate
    2005. Doctoral Dissertation Proposal, University of Texas at Austin.
  111. Learning for Semantic Parsing Using Statistical Machine Translation Techniques
    [Details] [PDF]
    Yuk Wah Wong
    2005. Doctoral Dissertation Proposal, University of Texas at Austin.
  112. An Expected Utility Approach to Active Feature-value Acquisition
    [Details] [PDF]
    P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney
    In Proceedings of the International Conference on Data Mining, 745-748, Houston, TX, November 2005.
  113. Combining Bias and Variance Reduction Techniques for Regression
    [Details] [PDF]
    Yuk Lai Suen, Prem Melville and Raymond J. Mooney
    Technical Report UT-AI-TR-05-321, University of Texas at Austin, July 2005. www.cs.utexas.edu/~ml/publication.
  114. Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping
    [Details] [PDF]
    Mikhail Bilenko, Sugato Basu, and Mehran Sahami
    In Proceedings of the 5th International Conference on Data Mining (ICDM-2005), 58--65, Houston, TX, November 2005.
  115. Alignments and String Similarity in Information Integration: A Random Field Approach
    [Details] [PDF]
    Mikhail Bilenko and Raymond J. Mooney
    In Proceedings of the 2005 Dagstuhl Seminar on Machine Learning for the Semantic Web, Dagstuhl, Germany, February 2005.
  116. A Shortest Path Dependency Kernel for Relation Extraction
    [Details] [PDF]
    R. C. Bunescu, and Raymond J. Mooney
    In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-05), 724-731, Vancouver, BC, October 2005.
  117. Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments
    [Details] [PDF]
    Sugato Basu
    PhD Thesis, University of Texas at Austin, 2005.
  118. Combining Bias and Variance Reduction Techniques for Regression
    [Details] [PDF]
    Y. L. Suen, P. Melville and Raymond J. Mooney
    In Proceedings of the 16th European Conference on Machine Learning, 741-749, Porto, Portugal, October 2005.
  119. Consolidating the Set of Known Human Protein-Protein Interactions in Preparation for Large-Scale Mapping of the Human Interactome
    [Details] [PDF]
    A.K. Ramani, R.C. Bunescu, Raymond J. Mooney and E.M. Marcotte
    Genome Biology, 6(5):r40, 2005.
  120. A Statistical Semantic Parser that Integrates Syntax and Semantics
    [Details] [PDF]
    Ruifang Ge and Raymond J. Mooney
    In Proceedings of CoNLL-2005, Ann Arbor, Michigan, June 2005.
  121. Mining Knowledge from Text Using Information Extraction
    [Details] [PDF]
    Raymond J. Mooney and R. Bunescu
    SIGKDD Explorations (special issue on Text Mining and Natural Language Processing), 7(1):3-10, 2005.
  122. Economical Active Feature-value Acquisition through Expected Utility Estimation
    [Details] [PDF]
    P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney
    In Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, 10-16, Chicago, IL, August 2005.
  123. Semi-supervised Graph Clustering: A Kernel Approach
    [Details] [PDF]
    B. Kulis, S. Basu, I. Dhillon and Raymond J. Mooney
    In Proceedings of the 22nd International Conference on Machine Learning, 457--464, Bonn, Germany, August 2005. (Distinguished Student Paper Award).
  124. Using Biomedical Literature Mining to Consolidate the Set of Known Human Protein-Protein Interactions
    [Details] [PDF]
    A. Ramani, E. Marcotte, R. Bunescu and Raymond J. Mooney
    In Proceedings of the ISMB/ACL-05 Workshop of the BioLINK SIG: Linking Literature, Information and Knowledge for Biology, Detroit, MI, June 2005.
  125. Model-based Overlapping Clustering
    [Details] [PDF]
    A. Banerjee, C. Krumpelman, S. Basu, Raymond J. Mooney and Joydeep Ghosh
    In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-05), 2005.
  126. Towards Self-Configuring Hardware for Distributed Computer Systems
    [Details] [PDF]
    Jonathan Wildstrom, Peter Stone, E. Witchel, Raymond Mooney and M. Dahlin
    In The Second International Conference on Autonomic Computing, 241-249, June 2005.
  127. Active Learning for Probability Estimation using Jensen-Shannon Divergence
    [Details] [PDF]
    P. Melville, S. M. Yang, M. Saar-Tsechansky and Raymond J. Mooney
    In Proceedings of the 16th European Conference on Machine Learning, 268--279, Porto, Portugal, October 2005.
  128. Learning to Transform Natural to Formal Languages
    [Details] [PDF] [Slides]
    Rohit J. Kate, Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), 1062-1068, Pittsburgh, PA, July 2005.
  129. Explaining Recommendations: Satisfaction vs. Promotion
    [Details] [PDF]
    Mustafa Bilgic and Raymond J. Mooney
    In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, January 2005.
  130. Learning for Collective Information Extraction
    [Details] [PDF]
    Razvan C. Bunescu
    Technical Report TR-05-02, Department of Computer Sciences, University of Texas at Austin, October 2005. Ph.D. proposal.
  131. Comparative Experiments on Learning Information Extractors for Proteins and their Interactions
    [Details] [PDF]
    Razvan Bunescu, Ruifang Ge, Rohit J. Kate, Edward M. Marcotte, Raymond J. Mooney, Arun Kumar Ramani, and Yuk Wah Wong
    Artificial Intelligence in Medicine (special issue on Summarization and Information Extraction from Medical Documents)(2):139-155, 2005.
  132. Active Feature-Value Acquisition for Classifier Induction
    [Details] [PDF]
    Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney
    Technical Report UT-AI-TR-04-311, Artificial Intelligence Lab, University of Texas at Austin, February 2004.
  133. Active Feature-Value Acquisition for Classifier Induction
    [Details] [PDF]
    Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney
    In Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM-2004), 483-486, Brighton, UK, November 2004.
  134. A Probabilistic Framework for Semi-Supervised Clustering
    [Details] [PDF]
    Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney
    In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), 59-68, Seattle, WA, August 2004.
  135. Text Mining with Information Extraction
    [Details] [PDF]
    Un Yong Nahm
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2004. 217 pages. Also appears as Technical Report UT-AI-TR-04-311.
  136. Collective Information Extraction with Relational Markov Networks
    [Details] [PDF]
    Razvan Bunescu and Raymond J. Mooney
    In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), 439-446, Barcelona, Spain, July 2004.
  137. Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer
    [Details] [PDF]
    Gregory Kuhlmann, Peter Stone, Raymond J. Mooney, and Jude W. Shavlik
    In The AAAI-2004 Workshop on Supervisory Control of Learning and Adaptive Systems, July 2004.
  138. Using Soft-Matching Mined Rules to Improve Information Extraction
    [Details] [PDF]
    Un Yong Nahm and Raymond J. Mooney
    In Proceedings of the AAAI-2004 Workshop on Adaptive Text Extraction and Mining (ATEM-2004), 27-32, San Jose, CA, July 2004.
  139. Semi-supervised Clustering with Limited Background Knowledge
    [Details] [PDF]
    Sugato Basu
    In Proceedings of the Ninth AAAI/SIGART Doctoral Consortium, 979--980, San Jose, CA, July 2004.
  140. Learnable Similarity Functions and Their Applications to Clustering and Record Linkage
    [Details] [PDF]
    Mikhail Bilenko
    In Proceedings of the Ninth AAAI/SIGART Doctoral Consortium, 981--982, San Jose, CA, July 2004.
  141. Integrating Constraints and Metric Learning in Semi-Supervised Clustering
    [Details] [PDF]
    Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney
    In Proceedings of 21st International Conference on Machine Learning (ICML-2004), 81-88, Banff, Canada, July 2004.
  142. Diverse Ensembles for Active Learning
    [Details] [PDF]
    Prem Melville and Raymond J. Mooney
    In Proceedings of 21st International Conference on Machine Learning (ICML-2004), 584-591, Banff, Canada, July 2004.
  143. Relational Markov Networks for Collective Information Extraction
    [Details] [PDF]
    Razvan Bunescu and Raymond J. Mooney
    In Proceedings of the ICML-04 Workshop on Statistical Relational Learning and its Connections to Other Fields, Banff, Alberta, July 2004.
  144. A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields
    [Details] [PDF]
    Mikhail Bilenko and Sugato Basu
    In Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004), Banff, Canada, July 2004.
  145. Experiments on Ensembles with Missing and Noisy Data
    [Details] [PDF]
    Prem Melville, Nishit Shah, Lilyana Mihalkova, and Raymond J. Mooney
    In F. Roli, J. Kittler, and T. Windeatt, editors, {Lecture Notes in Computer Science:} Proceedings of the Fifth International Workshop on Multi Classifier Systems (MCS-2004), 293-302, Cagliari, Italy, June 2004. Springer Verlag.
  146. Explanation for Recommender Systems: Satisfaction vs. Promotion
    [Details] [PDF]
    Mustafa Bilgic
    Austin, TX, May 2004. Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.
  147. Active Semi-Supervision for Pairwise Constrained Clustering
    [Details] [PDF]
    Sugato Basu, Arindam Banerjee, and Raymond J. Mooney
    In Proceedings of the 2004 SIAM International Conference on Data Mining (SDM-04), April 2004.
  148. Learning Transformation Rules for Semantic Parsing
    [Details] [PDF]
    Rohit J. Kate, Yuk Wah Wong, Ruifang Ge, and Raymond J. Mooney
    April 2004. Unpublished Technical Report.
  149. Creating Diversity in Ensembles Using Artificial Data
    [Details] [PDF]
    Prem Melville and Raymond J. Mooney
    Journal of Information Fusion: Special Issue on Diversity in Multi Classifier Systems, 6(1):99-111, 2004.
  150. Learning Semantic Parsers: An Important But Under-Studied Problem
    [Details] [PDF]
    Raymond J. Mooney
    In Papers from the AAAI 2004 Spring Symposium on Language Learning: An Interdisciplinary Perspective, 39--44, Stanford, CA, March 2004.
  151. Relational Data Mining with Inductive Logic Programming for Link Discovery
    [Details] [PDF]
    Raymond J. Mooney, P. Melville, L. R. Tang, J. Shavlik, I. Dutra and D. Page
    Kargupta, H., Joshi, A., Sivakumar K., and Yesha, Y., editors, Data Mining: Next Generation Challenges and Future Directions:239--254, Menlo Park, CA, 2004. AAAI Press.
  152. Semisupervised Clustering for Intelligent User Management
    [Details] [PDF]
    Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney
    In Proceedings of the IBM Austin Center for Advanced Studies 5th Annual Austin CAS Conference, Austin, TX, February 2004.
  153. Semi-supervised Clustering: Learning with Limited User Feedback
    [Details] [PDF]
    Sugato Basu
    Technical Report, Cornell University, 2004.
  154. Text Mining with Information Extraction
    [Details] [PDF]
    Raymond J. Mooney and Un Yong Nahm
    In W. Daelemans and T. du Plessis and C. Snyman and L. Teck, editors, Multilingualism and Electronic Language Management: Proceedings of the 4th International MIDP Colloquium, 141-160, Bloemfontein, South Africa, September 2003. Van Schaik: South Africa.
  155. Learnable Similarity Functions and Their Applications to Record Linkage and Clustering
    [Details] [PDF]
    Mikhail Bilenko
    2003. Doctoral Dissertation Proposal, University of Texas at Austin.
  156. Creating Diverse Ensemble Classifiers
    [Details] [PDF]
    Prem Melville
    Technical Report UT-AI-TR-03-306, Department of Computer Sciences, University of Texas at Austin, December 2003. Ph.D. proposal.
  157. Adaptive Name-Matching in Information Integration
    [Details] [PDF]
    Mikhail Bilenko, William W. Cohen, Stephen Fienberg, Raymond J. Mooney, and Pradeep Ravikumar
    IEEE Intelligent Systems, 18(5):16-23, 2003.
  158. Integrating Top-down and Bottom-up Approaches in Inductive Logic Programming: Applications in Natural Language Processing and Relational Data Mining
    [Details] [PDF]
    Lappoon R. Tang
    PhD Thesis, Department of Computer Sciences, University of Texas, Austin, TX, August 2003.
  159. On Evaluation and Training-Set Construction for Duplicate Detection
    [Details] [PDF]
    Mikhail Bilenko and Raymond J. Mooney
    In Proceedings of the KDD-03 Workshop on Data Cleaning, Record Linkage, and Object Consolidation, 7-12, Washington, DC, August 2003.
  160. Scaling Up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism
    [Details] [PDF]
    Lappoon R. Tang, Raymond J. Mooney, and Prem Melville
    In Proceedings of the KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), 107--121, Washington DC, August 2003.
  161. Adaptive Duplicate Detection Using Learnable String Similarity Measures
    [Details] [PDF]
    Mikhail Bilenko and Raymond J. Mooney
    In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003), 39-48, Washington, DC, August 2003.
  162. Learning to Extract Proteins and their Interactions from Medline Abstracts
    [Details] [PDF]
    Razvan Bunescu, Ruifang Ge, Rohit J. Kate, Raymond J. Mooney, Yuk Wah Wong, Edward M. Marcotte, and Arun Kumar Ramani
    In Proceedings of the ICML-03 Workshop on Machine Learning in Bioinformatics, 46-53, Washington, DC, August 2003.
  163. Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering
    [Details] [PDF]
    Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney
    In Proceedings of the ICML-2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, 42-49, Washington, DC, 2003.
  164. Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    Journal of Machine Learning Research:177-210, 2003.
  165. Employing Trainable String Similarity Metrics for Information Integration
    [Details] [PDF]
    Mikhail Bilenko and Raymond J. Mooney
    In Proceedings of the IJCAI-03 Workshop on Information Integration on the Web, 67-72, Acapulco, Mexico, August 2003.
  166. Constructing Diverse Classifier Ensembles Using Artificial Training Examples
    [Details] [PDF]
    Prem Melville and Raymond J. Mooney
    In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-2003), 505-510, Acapulco, Mexico, August 2003.
  167. Acquiring Word-Meaning Mappings for Natural Language Interfaces
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    Journal of Artificial Intelligence Research, 18:1-44, 2003.
  168. Associative Anaphora Resolution: A Web-Based Approach
    [Details] [PDF]
    Razvan Bunescu
    In Proceedings of the EACL-2003 Workshop on the Computational Treatment of Anaphora, 47-52, Budapest, Hungary, 2003.
  169. Machine Learning
    [Details] [PDF]
    Raymond J. Mooney
    New York, NY, 2003. McGraw-Hill.
  170. Property-Based Feature Engineering and Selection
    [Details] [PDF]
    Noppadon Kamolvilassatian
    Masters Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, December 2002. 85 pages.
  171. Mining Soft-Matching Association Rules
    [Details] [PDF]
    Un Yong Nahm and Raymond J. Mooney
    In Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM-2002), 681-683, McLean, VA, November 2002.
  172. Relational Data Mining with Inductive Logic Programming for Link Discovery
    [Details] [PDF]
    Raymond J. Mooney, Prem Melville, Lappoon R. Tang, Jude Shavlik, Inês de Castro Dutra, David Page, and Vítor Santos Costa
    In Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, MD, November 2002.
  173. Two Approaches to Handling Noisy Variation in Text Mining
    [Details] [PDF]
    Un Yong Nahm, Mikhail Bilenko, and Raymond J. Mooney
    In Papers from the Nineteenth International Conference on Machine Learning (ICML-2002) Workshop on Text Learning, 18-27, Sydney, Australia, July 2002.
  174. Content-Boosted Collaborative Filtering for Improved Recommendations
    [Details] [PDF]
    Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
    In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), 187-192, Edmonton, Alberta, 2002.
  175. Semi-supervised Clustering by Seeding
    [Details] [PDF]
    Sugato Basu, Arindam Banerjee, and Raymond J. Mooney
    In Proceedings of 19th International Conference on Machine Learning (ICML-2002), 19-26, 2002.
  176. Text Mining with Information Extraction
    [Details] [PDF]
    Un Yong Nahm and Raymond J. Mooney
    In Proceedings of the AAAI 2002 Spring Symposium on Mining Answers from Texts and Knowledge Bases, 60-67, Stanford, CA, March 2002.
  177. Extracting Gene and Protein Names from Biomedical Abstracts
    [Details] [PDF]
    Razvan Bunescu, Ruifang Ge, Raymond J. Mooney, Edward Marcotte, and Arun Kumar Ramani
    March 2002. Unpublished Technical Note.
  178. Learning to Combine Trained Distance Metrics for Duplicate Detection in Databases
    [Details] [PDF]
    Mikhail Bilenko and Raymond J. Mooney
    Technical Report AI 02-296, Artificial Intelligence Laboratory, University of Texas at Austin, Austin, TX, February 2002.
  179. ELIXIR: A Library for Writing Wrappers in Java
    [Details] [PDF]
    Edward Wild
    , December 2001. Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.
  180. Content-Boosted Collaborative Filtering
    [Details] [PDF]
    Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
    In Proceedings of the SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA, September 2001.
  181. Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing
    [Details] [PDF]
    Lappoon R. Tang and Raymond J. Mooney
    In Proceedings of the 12th European Conference on Machine Learning, 466-477, Freiburg, Germany, 2001.
  182. Evaluating the Novelty of Text-Mined Rules using Lexical Knowledge
    [Details] [PDF]
    Sugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, and Joydeep Ghosh
    In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), 233-239, San Francisco, CA, 2001.
  183. Mining Soft-Matching Rules from Textual Data
    [Details] [PDF]
    Un Yong Nahm and Raymond J. Mooney
    In Proceedings of the 18th International Joint Conference on Artificial Intelligence, 2001.
  184. Using Lexical Knowlege to Evaluate the Novelty of Rules Mined from Text
    [Details] [PDF]
    Sugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, and Joydeep Ghosh
    In Proceedings of NAACL 2001 Workshop on WordNet and Other Lexical Resources: Applications, Extensions and Customizations, 144--149, Pittsburg, PA, June 2001.
  185. Text Mining with Information Extraction
    [Details] [PDF]
    Un Yong Nahm
    February 2001. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  186. Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing
    [Details] [PDF]
    Lappoon R. Tang and Raymond J. Mooney
    In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora(EMNLP/VLC-2000), 133-141, Hong Kong, October 2000.
  187. Using Information Extraction to Aid the Discovery of Prediction Rules from Text
    [Details] [PDF]
    Un Yong Nahm and Raymond J. Mooney
    In Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000) Workshop on Text Mining, 51--58, Boston, MA, August 2000.
  188. A Mutually Beneficial Integration of Data Mining and Information Extraction
    [Details] [PDF]
    Un Yong Nahm and Raymond J. Mooney
    In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), 627-632, Austin, TX, July 2000.
  189. Integrating Statistical and Relational Learning for Semantic Parsing: Applications to Learning Natural Language Interfaces for Databases
    [Details] [PDF]
    Lappoon R. Tang
    May 2000. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  190. Content-Based Book Recommending Using Learning for Text Categorization
    [Details] [PDF]
    Raymond J. Mooney and Loriene Roy
    In Proceedings of the Fifth ACM Conference on Digital Libraries, 195-204, San Antonio, TX, June 2000.
  191. Integrating Abduction and Induction in Machine Learning
    [Details] [PDF]
    Raymond J. Mooney
    In P. A. Flach and A. C. Kakas, editors, Abduction and Induction, 181-191, 2000. Kluwer Academic Publishers.
  192. Learning for Semantic Interpretation: Scaling Up Without Dumbing Down
    [Details] [PDF]
    Raymond J. Mooney
    In Workshop Notes for the Workshop on Learning Language in Logic, 7-15, Bled, Slovenia, 2000.
  193. Content-Based Book Recommending Using Learning for Text Categorization
    [Details] [PDF]
    Raymond J. Mooney and Loriene Roy
    In Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA, August 1999.
  194. Automatic Construction of Semantic Lexicons for Learning Natural Language Interfaces
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), 487-493, Orlando, FL, July 1999.
  195. Relational Learning of Pattern-Match Rules for Information Extraction
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), 328-334, Orlando, FL, July 1999.
  196. Active Learning for Natural Language Parsing and Information Extraction
    [Details] [PDF]
    Cynthia A. Thompson, Mary Elaine Califf and Raymond J. Mooney
    In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99), 406-414, Bled, Slovenia, June 1999.
  197. Using HTML Structure and Linked Pages to Improve Learning for Text Categorization
    [Details] [PDF]
    Michael B. Cline
    Technical Report AI 98-270, Department of Computer Sciences, University of Texas at Austin, Austin, TX, May 1999. Undergraduate Honors Thesis.
  198. Semantic Lexicon Acquisition for Learning Natural Language Interfaces
    [Details] [PDF]
    Cynthia Ann Thompson
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, December 1998. 101 pages. Also appears as Technical Report AI 99-278, Artificial Intelligence Lab, University of Texas at Austin.
  199. Semantic Lexicon Acquisition for Learning Natural Language Interfaces
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    In Proceedings of the Sixth Workshop on Very Large Corpora, Montreal, Quebec, Canada, August 1998. Also available as TR AI 98-273, Artificial Intelligence Lab, University of Texas at Austin, May 1998.
  200. Relational Learning Techniques for Natural Language Information Extraction
    [Details] [PDF]
    Mary Elaine Califf
    PhD Thesis, Department of Computer Sciences, University of Texas, Austin, TX, August 1998. 142 pages. Also appears as Artificial Intelligence Laboratory Technical Report AI 98-276.
  201. Theory Refinement for Bayesian Networks with Hidden Variables
    [Details] [PDF]
    Sowmya Ramachandran and Raymond J. Mooney
    In Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98), 454--462, Madison, WI, July 1998.
  202. Book Recommending Using Text Categorization with Extracted Information
    [Details] [PDF]
    Raymond J. Mooney, Paul N. Bennett, and Loriene Roy
    In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98)"-REC-WKSHP98, year="1998, 70-74, Madison, WI, 1998.
  203. Text Categorization Through Probabilistic Learning: Applications to Recommender Systems
    [Details] [PDF]
    Paul N. Bennett
    1998. Honors thesis, Department of Computer Sciences, The University of Texas at Austin.
  204. Theory Refinement of Bayesian Networks with Hidden Variables
    [Details] [PDF]
    Sowmya Ramachandran and Raymond J. Mooney
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, May 1998. 139 pages. Also appears as Technical Report AI 98-265, Artificial Intelligence Lab, University of Texas at Austin.
  205. An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions
    [Details] [PDF]
    Lappoon R. Tang, Mary Elaine Califf, and Raymond J. Mooney
    Technical Report AI 98-271, Artificial Intelligence Lab, University of Texas at Austin, May 1998.
  206. Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    New Generation Computing, 16(3):263-281, 1998.
  207. Using Multi-Strategy Learning to Improve Planning Efficiency and Quality
    [Details] [PDF]
    Tara A. Estlin
    PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1998.
  208. Relational Learning of Pattern-Match Rules for Information Extraction
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    In Proceedings of AAAI Spring Symposium on Applying Machine Learning to Discourse Processing, 6-11, Standford, CA, March 1998.
  209. Integrating Abduction and Induction in Machine Learning
    [Details] [PDF]
    Raymond J. Mooney
    In Working Notes of the IJCAI-97 Workshop on Abduction and Induction in AI, 37--42, Nagoya, Japan, August 1997.
  210. Relational Learning Techniques for Natural Language Information Extraction
    [Details] [PDF]
    Mary Elaine Califf
    1997. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  211. Learning to Improve both Efficiency and Quality of Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), 1227-1232, Nagoya, Japan, 1997.
  212. Applying ILP-based Techniques to Natural Language Information Extraction: An Experiment in Relational Learning
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    In Workshop Notes of the IJCAI-97 Workshop on Frontiers of Inductive Logic Programming, 7--11, Nagoya, Japan, August 1997.
  213. Learning to Parse Natural Language Database Queries into Logical Form
    [Details] [PDF]
    Cynthia A. Thompson, Raymond J. Mooney, and Lappoon R. Tang
    In Proceedings of the ML-97 Workshop on Automata Induction, Grammatical Inference, and Language Acquisition, Nashville, TN, July 1997.
  214. Relational Learning of Pattern-Match Rules for Information Extraction
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    In Proceedings of the ACL Workshop on Natural Language Learning, 9-15, Madrid, Spain, July 1997.
  215. Learning Parse and Translation Decisions From Examples With Rich Context
    [Details] [PDF]
    Ulf Hermjakob and Raymond J. Mooney
    In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL'97/EACL'97), 482-489, July 1997.
  216. Learning Parse and Translation Decisions From Examples With Rich Context
    [Details] [PDF]
    Ulf Hermjakob
    PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, May 1997. 175 pages. Technical Report UT-AI97-261.
  217. An Inductive Logic Programming Method for Corpus-based Parser Construction
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    January 1997. Unpublished Technical Note.
  218. Parameter Revision Techniques for Bayesian Networks with Hidden Variables: An Experimental Comparison
    [Details] [PDF]
    Sowmya Ramachandran and Raymond J. Mooney
    January 1997. Unpublished Technical Note.
  219. Semantic Lexicon Acquisition for Learning Parsers
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    1997. Submitted for review.
  220. Inductive Logic Programming for Natural Language Processing
    [Details] [PDF]
    Raymond J. Mooney
    In Stephen Muggleton, editors, Inductive Logic Programming: Selected papers from the 6th International Workshop, 3-22, Berlin, 1996. Springer Verlag.
  221. Integrating Explanation-Based and Inductive Learning Techniques to Acquire Search-Control for Planning
    [Details] [PDF]
    Tara A. Estlin
    Technical Report AI96-250, Department of Computer Sciences, University of Texas, Austin, TX, 1996.
  222. Learning to Parse Database Queries using Inductive Logic Programming
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In AAAI/IAAI, 1050-1055, Portland, OR, August 1996. AAAI Press/MIT Press.
  223. A Novel Application of Theory Refinement to Student Modeling
    [Details] [PDF]
    Paul Baffes and Raymond J. Mooney
    In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), 403-408, Portland, OR, August 1996.
  224. Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using Behavioral Modes
    [Details] [PDF]
    Siddarth Subramanian and Raymond J. Mooney
    In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), 965-970, Portland, OR, August 1996.
  225. Multi-Strategy Learning of Search Control for Partial-Order Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), 843-848, Portland, OR, August 1996.
  226. Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-96), 82-91, Philadelphia, PA, 1996.
  227. Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases
    [Details] [PDF]
    J. Jeffrey Mahoney
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, May 1996. 113 pages.
  228. Integrating EBL and ILP to Acquire Control Rules for Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96), 271--279, Harpers Ferry, WV, May 1996.
  229. Refinement-Based Student Modeling and Automated Bug Library Construction
    [Details] [PDF]
    Paul Baffes and Raymond Mooney
    Journal of Artificial Intelligence in Education, 7(1):75-116, 1996.
  230. Revising Bayesian Network Parameters Using Backpropagation
    [Details] [PDF]
    Sowmya Ramachandran and Raymond J. Mooney
    In Proceedings of the International Conference on Neural Networks (ICNN-96), Special Session on Knowledge-Based Artificial Neural Networks, 82--87, Washington DC, June 1996.
  231. Corpus-Based Lexical Acquisition For Semantic Parsing
    [Details] [PDF]
    Cynthia Thompson
    February 1996. Ph.D. proposal.
  232. Lexical Acquisition: A Novel Machine Learning Problem
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    Technical Report, Artificial Intelligence Lab, University of Texas at Austin, January 1996.
  233. Advantages of Decision Lists and Implicit Negative in Inductive Logic Programming
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    Technical Report, Artificial Intelligence Lab, University of Texas at Austin, January 1996.
  234. Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Stefan Wermter and Ellen Riloff and Gabriela Scheler, editors, Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, 355-369, Berlin, 1996. Springer.
  235. Learning the Past Tense of English Verbs Using Inductive Logic Programming
    [Details] [PDF]
    Raymond J. Mooney and Mary Elaine Califf
    In {S. Wermter, E. Riloff} and G. Scheler, editors, Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, 370-384, Berlin, 1996. Springer.
  236. Hybrid Learning of Search Control for Partial-Order Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Malik Ghallab and Alfredo Milani, editors, New Directions in AI Planning, 129-140, Amsterdam, 1996. IOS Press.
  237. Refinement of Bayesian Networks by Combining Connectionist and Symbolic Techniques
    [Details] [PDF]
    Sowmya Ramachandran
    , 1995. Unpublished Ph.D. Thesis Proposal.
  238. Inducing Logic Programs without Explicit Negative Examples
    [Details] [PDF]
    John M. Zelle, Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney
    In Proceedings of the Fifth International Workshop on Inductive Logic Programming (ILP-95), 403-416, Leuven, Belgium, 1995.
  239. Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using Behavioral Modes
    [Details] [PDF]
    Siddarth Subramanian
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, 1995. 128 pages. Also appears as Technical Report AI 95-239.
  240. Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers
    [Details] [PDF]
    John M. Zelle
    PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1995.
  241. A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Working Notes of the IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing, 79--86, Montreal, Quebec, Canada, August 1995.
  242. Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes
    [Details] [PDF]
    Siddarth Subramanian and Raymond J. Mooney
    In Working Notes of the IJCAI-95 Workshop on Engneering Problems for Qualitative Reasoning, Monreal, Quebec, August 1995.
  243. Acquisition of a Lexicon from Semantic Representations of Sentences
    [Details] [PDF]
    Cynthia A. Thompson
    In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL-95), 335-337, Cambridge, MA, 1995.
  244. Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs
    [Details] [PDF]
    Raymond J. Mooney and Mary Elaine Califf
    Journal of Artificial Intelligence Research, 3:1-24, 1995.
  245. Automated Refinement of First-Order Horn-Clause Domain Theories
    [Details] [PDF]
    Bradley L. Richards and Raymond J. Mooney
    Machine Learning, 19(2):95-131, 1995.
  246. Encouraging Experimental Results on Learning CNF
    [Details] [PDF]
    Raymond J. Mooney
    Machine Learning, 19(1):79-92, 1995.
  247. A Preliminary PAC Analysis of Theory Revision
    [Details] [PDF]
    Raymond J. Mooney
    In T. Petsche and S. Hanson and Jude W. Shavlik, editors, Computational Learning Theory and Natural Learning Systems, Vol. 3, 43-53, Cambridge, MA, 1995. MIT Press.
  248. Automatic Student Modeling and Bug Library Construction using Theory Refinement
    [Details] [PDF]
    Paul T. Baffes
    PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1994.
  249. Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes
    [Details] [PDF]
    Siddarth Subramanian and Raymond J. Mooney
    In Working Papers of the Fifth International Workshop on Principles of Diagnosis, 321-325, New Paltz, NY, October 1994.
  250. Inductive Learning For Abductive Diagnosis
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), 664-669, Seattle, WA, August 1994.
  251. Comparing Methods For Refining Certainty Factor Rule-Bases
    [Details] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), 173--180, Rutgers, NJ, July 1994.
  252. Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming
    [Details] [PDF]
    John M. Zelle, Raymond J. Mooney, and Joshua B. Konvisser
    In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), 343--351, Rutgers, NJ, July 1994.
  253. Inducing Deterministic Prolog Parsers From Treebanks: A Machine Learning Approach
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), 748--753, Seattle, WA, July 1994.
  254. Learning Qualitative Models for Systems with Multiple Operating Regions
    [Details] [PDF]
    Sowmya Ramachandran, Raymond J. Mooney, and Benjamin J. Kuipers
    In Proceedings of the Eighth International Workshop on Qualitative Reasoning about Physical Systems, Nara, Japan, 1994.
  255. Modifying Network Architectures For Certainty-Factor Rule-Base Revision
    [Details] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    In Proceedings of the International Symposium on Integrating Knowledge and Neural Heuristics (ISIKNH-94), 75--85, Pensacola, FL, May 1994.
  256. A Multistrategy Approach to Theory Refinement
    [Details] [PDF]
    Raymond J. Mooney and Dirk Ourston
    In Ryszard S. Michalski and G. Teccuci, editors, Machine Learning: A Multistrategy Approach, Vol. IV, 141-164, San Mateo, CA, 1994. Morgan Kaufmann.
  257. Theory Refinement Combining Analytical and Empirical Methods
    [Details] [PDF]
    Dirk Ourston and Raymond J. Mooney
    Artificial Intelligence:311-344, 1994.
  258. Integrating ILP and EBL
    [Details] [PDF]
    Raymond J. Mooney and John M. Zelle
    Sigart Bulletin (special issue on Inductive Logic Programmming), 5(1):12-21, 1994.
  259. Extending Theory Refinement to M-of-N Rules
    [Details] [PDF]
    Paul T. Baffes and Raymond J. Mooney
    Informatica, 17:387-397, 1993.
  260. Inductive Learning For Abductive Diagnosis
    [Details] [PDF]
    Cynthia A. Thompson
    Masters Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, August 1993. 53 pages.
  261. Combining FOIL and EBG to Speed-Up Logic Programs
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1106-1111, 1993. San Francisco, CA: Morgan Kaufmann.
  262. Symbolic Revision of Theories With M-of-N Rules
    [Details] [PDF]
    Paul T. Baffes and Raymond J. Mooney
    In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), 1135-1140, Chambery, France, August 1993.
  263. Learning Semantic Grammars With Constructive Inductive Logic Programming
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 11th National Conference on Artificial Intelligence, 817-822, 1993. Menlo Park, CA: AAAI Press.
  264. Learning to Model Students: Using Theory Refinement to Detect Misconceptions
    [Details] [PDF]
    Paul T. Baffes
    June 1993. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  265. Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule-Bases
    [Details] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    Connection Science:339-364, 1993.
  266. Integrating Theory and Data in Category Learning
    [Details] [PDF]
    Raymond J. Mooney
    In G. V. Nakamura and D. L. Medin and R. Taraban, editors, Categorization by Humans and Machines, 189-218, 1993.
  267. Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition
    [Details] [PDF]
    John M. Zelle
    March 1993. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  268. Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning
    [Details] [PDF]
    Raymond J. Mooney
    Machine Learning, 10:79-110, 1993.
  269. An Operator-Based Approach to First-Order Theory Revision
    [Details] [PDF]
    Bradley Lance Richards
    PhD Thesis, Department of Computer Science, University of Texas at Austin, August 1992.
  270. A General Abductive system with application to plan recognition and diagnosis
    [Details] [PDF]
    Hwee Tou Ng
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, May 1992. 154 pages.
  271. Schema acquisition from a single example
    [Details] [PDF]
    W. Ahn, W. F. Brewer and Raymond J. Mooney
    Journal of Experimental Psychology: Learning, Memory, and Cognition, 18:391-412, 1992.
  272. Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation
    [Details] [PDF]
    Hwee Tou Ng and Raymond J. Mooney
    In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning, 499--508, Cambridge, MA, October 1992.
  273. Speeding-up Logic Programs by Combining EBG and FOIL
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 1992 Machine Learning Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, Scotland, July 1992.
  274. Automatic Abduction of Qualitative Models
    [Details] [PDF]
    Bradley L. Richards, Ina Kraan, and Benjamin J. Kuipers
    In Proceedings of the Fifth International Workshop on Qualitative Reasoning about Physical Systems, 295-301, 1992.
  275. Using Theory Revision to Model Students and Acquire Stereotypical Errors
    [Details] [PDF]
    Paul T. Baffes and Raymond J. Mooney
    In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, 617-622, Bloomington, IN, 1992.
  276. Learning Relations by Pathfinding
    [Details] [PDF]
    Bradley L. Richards and Raymond J. Mooney
    In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), 50-55, San Jose, CA, July 1992.
  277. Combining Symbolic and Neural Learning to Revise Probabilistic Theories
    [Details] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    In Proceedings of the ML92 Workshop on Integrated Learning in Real Domains, Aberdeen, Scotland, July 1992.
  278. A First-Order Horn-Clause Abductive System and Its Use in Plan Recognition and Diagnosis
    [Details] [PDF]
    Hwee Tou Ng and Raymond J. Mooney
    June 1992. Unpublished Technical Note.
  279. Growing Layers of Perceptrons: Introducing the Extentron Algorithm
    [Details] [PDF]
    Paul T. Baffes and John M. Zelle
    In Proceedings of the 1992 International Joint Conference on Neural Networks, 392--397, Baltimore, MD, June 1992.
  280. Automated Debugging of Logic Programs via Theory Revision
    [Details] [PDF]
    Raymond J. Mooney and Bradley L. Richards
    In Proceedings of the Second International Workshop on Inductive Logic Programming (ILP-92), Tokyo, Japan, 1992.
  281. Belief Revision in the Context of Abductive Explanation
    [Details] [PDF]
    Siddarth Subramanian
    Technical Report AI92-179, Artificial Intelligence Laboratory, University of Texas, Austin, TX, December 1992.
  282. Batch versus Incremental Theory Refinement
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the 1992 AAAI Spring Symposium on Knowledge Assimilation, Standford, CA, March 1992.
  283. Using Explanation-Based and Empirical Methods in Theory Revision
    [Details] [PDF]
    Dirk Ourston
    PhD Thesis, Department of Computer Science, University of Texas at Austin, 1991.
  284. First-Order Theory Revision
    [Details] [PDF]
    Bradley L. Richards and Raymond J. Mooney
    In Proceedings of the Eighth International Machine Learning Workshop, pp. 447-451, Evanston, IL, June 1991.
  285. Symbolic and Neural Learning Algorithms: An Experimental Comparison
    [Details] [PDF]
    J.W. Shavlik, Raymond J. Mooney and G. Towell
    Machine Learning, 6:111-143, 1991. Reprinted in {it Readings in Knowledge Acquisition and Learning}, Bruce G. Buchanan and David C. Wilkins (eds.), Morgan Kaufman, San Mateo, CA, 1993..
  286. An Efficient First-Order Horn-Clause Abduction System Based on the ATMS
    [Details] [PDF]
    Hwee Tou Ng and Raymond J. Mooney
    In Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), 494-499, Anaheim, CA, July 1991.
  287. Improving Shared Rules in Multiple Category Domain Theories
    [Details] [PDF]
    Dirk Ourston and Raymond J. Mooney
    In Proceedings of the Eighth International Workshop on Machine Learning, 534-538, Evanston, IL, June 1991.
  288. Constructive Induction in Theory Refinement
    [Details] [PDF]
    Raymond J. Mooney and Dirk Ourston
    In Proceedings of the Eighth International Workshop on Machine Learning, 178-182, Evanston, IL, June 1991.
  289. Theory Refinement with Noisy Data
    [Details] [PDF]
    Raymond J. Mooney and Dirk Ourston
    Technical Report AI91-153, Artificial Intelligence Laboratory, University of Texas, Austin, TX, March 1991.
  290. Changing the Rules: A Comprehensive Approach to Theory Refinement
    [Details] [PDF]
    D. Ourston and Raymond J. Mooney
    In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), 815-820, Boston, MA, July 1990.
  291. On the Role of Coherence in Abductive Explanation
    [Details] [PDF]
    Hwee Tou Ng and Raymond J. Mooney
    In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), 337--342, Boston, MA, July 1990.
  292. Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition
    [Details] [PDF]
    Raymond J. Mooney
    Cognitive Science, 14(4):483-509, 1990.
  293. Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems
    [Details] [PDF]
    Douglas Fisher and Kathleen McKusick and Raymond J. Mooney and Jude W. Shavlik and Geoffrey Towell
    In Proceedings of the Sixth International Workshop on Machine Learning, 169--173, Ithaca, New York, 1989.
  294. An Experimental Comparison of Symbolic and Connectionist Learning Algorithms
    [Details] [PDF]
    Raymond J. Mooney, J.W. Shavlik, G. Towell and A. Gove
    In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), 775-780, Detroit, MI, August 1989. Reprinted in ``Readings in Machine Learning'', Jude W. Shavlik and T. G. Dietterich (eds.), Morgan Kaufman, San Mateo, CA, 1990..
  295. The Effect of Rule Use on the Utility of Explanation-Based Learning
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the 11th International Joint Conference on Artificial Intelligence, 725-730, 1989. San Francisco, CA: Morgan Kaufmann.
  296. Generalizing the Order of Operators in Macro-Operators
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the Fifth International Conference on Machine Learning (ICML-88), 270-283, Ann Arbor, MI, June 1988.
  297. A General Explanation-Based Learning Mechanism and its Application to Narrative Understanding
    [Details] [PDF]
    Raymond J. Mooney
    Ph.D. thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, 1988
  298. Integrated Learning of Words and their Underlying Concepts
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, 947-978, Seattle, WA, July 1987.
  299. Schema Acquisition from One Example: Psychological Evidence for Explanation-Based Learning
    [Details] [PDF]
    W. Ahn, Raymond J. Mooney, W.F. Brewer and G.F. DeJong
    In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, 50-57, Seattle, WA, July 1987.
  300. A Domain Independent Explanation-Based Generalizer
    [Details] [PDF]
    Raymond J. Mooney and S.W. Bennett
    In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), 551-555, Philadelphia, PA, August 1986.
  301. Explanation-Based Learning: An Alternative View
    [Details] [PDF]
    G.F. DeJong and Raymond J. Mooney
    Machine Learning:145-176, 1986.
  302. Generalizing Explanations of Narratives into Schemata
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the Third International Machine Learning Workshop, 126--128, New Brunswick, New Jersey, 1985.
  303. Learning Schemata for Natural Language Processing
    [Details] [PDF]
    Raymond J. Mooney and Gerald F. DeJong
    In Proceedings of the Ninth International Joint Conference on Artificial Intelligence (IJCAI-85), 681-687, Los Angeles, CA, August 1985.
  304. Generalizing Explanations of Narratives into Schemata
    [Details] [PDF]
    Raymond J. Mooney
    Masters Thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, 1985.