Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: Natural Language Learning

Natural language processing systems are difficult to build, and machine learning methods can help automate their construction significantly. Our research in learning for natural language has mainly involved applying statistical relational learning, inductive logic programming, explanation-based learning, and other learning techniques to automatically construct semantic parsers (e.g. database interfaces) and information extraction systems from training examples. However, we have also conducted research in learning for syntactic parsing, machine translation, language generation, word-sense disambiguation, morphology (past tense generation), and schema-based narrative understanding. Our on-going work is focussed on learning to connect language and perception.

Sub-areas:

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  1. Online Inference-Rule Learning from Natural-Language Extractions
    [Details] [PDF]
    Sindhu Raghavan and Raymond J. Mooney
    In Proceedings of the 3rd Statistical Relational AI (StaRAI-13) workshop at AAAI '13, July 2013.
  2. Adapting Discriminative Reranking to Grounded Language Learning
    [Details] [PDF]
    Joohyun Kim and Raymond J. Mooney
    In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013), Sofia, Bulgaria, August 2013.
  3. Montague Meets Markov: Deep Semantics with Probabilistic Logical Form
    [Details] [PDF]
    Islam Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond Mooney
    To Appear In Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*Sem-2013), June 2013.
  4. A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics
    [Details] [PDF]
    Dan Garrette, Katrin Erk, Raymond J. Mooney
    Harry Bunt, Johan Bos, and Stephen Pulman, editors, Computing Meaning, 4 (in press), 2013.
  5. Learning a Part-of-Speech Tagger from Two Hours of Annotation
    [Details] [PDF]
    Dan Garrette, Jason Baldridge
    To Appear In Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-13), June 2013.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. 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.
  54. Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques
    [Details] [PDF]
    Ruifang Ge
    2006. Doctoral Dissertation Proposal, University of Texas at Austin" , year="2006.
  55. 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}.
  56. A Kernel-based Approach to Learning Semantic Parsers
    [Details] [PDF] [Slides]
    Rohit J. Kate
    2005. Doctoral Dissertation Proposal, University of Texas at Austin.
  57. Learning for Semantic Parsing Using Statistical Machine Translation Techniques
    [Details] [PDF]
    Yuk Wah Wong
    2005. Doctoral Dissertation Proposal, University of Texas at Austin.
  58. 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.
  59. 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.
  60. 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.
  61. 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.
  62. 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.
  63. 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.
  64. 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.
  65. 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.
  66. 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.
  67. 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.
  68. Learning Transformation Rules for Semantic Parsing
    [Details] [PDF]
    Rohit J. Kate, Yuk Wah Wong, Ruifang Ge, and Raymond J. Mooney
    In A. Gelbukh, editors, Computational Linguistics and Intelligent Text Processing: Proceedings of the 8th International Conference, CICLing 2007, Mexico City, 311-324, Berlin, 2004. Springer Verlag.
  69. 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.
  70. 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.
  71. 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.
  72. 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.
  73. 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.
  74. 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.
  75. Machine Learning
    [Details] [PDF]
    Raymond J. Mooney
    New York, NY, 2003. McGraw-Hill.
  76. 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.
  77. 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.
  78. 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.
  79. 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.
  80. 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.
  81. 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.
  82. 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.
  83. 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.
  84. 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.
  85. 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.
  86. 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.
  87. 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.
  88. 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.
  89. 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.
  90. 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.
  91. 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.
  92. 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.
  93. 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.
  94. An Inductive Logic Programming Method for Corpus-based Parser Construction
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    January 1997. Unpublished Technical Note.
  95. Semantic Lexicon Acquisition for Learning Parsers
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    1997. Submitted for review.
  96. 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.
  97. 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.
  98. 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.
  99. Corpus-Based Lexical Acquisition For Semantic Parsing
    [Details] [PDF]
    Cynthia Thompson
    February 1996. Ph.D. proposal.
  100. 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.
  101. 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.
  102. 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.
  103. 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.
  104. 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.
  105. 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.
  106. Acquisition of a Lexicon from Semantic Representations of Sentences
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    Cynthia A. Thompson
    In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL-95), 335-337, Cambridge, MA, 1995.
  107. Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs
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    Raymond J. Mooney and Mary Elaine Califf
    Journal of Artificial Intelligence Research, 3:1-24, 1995.
  108. Inducing Deterministic Prolog Parsers From Treebanks: A Machine Learning Approach
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    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.
  109. Learning Semantic Grammars With Constructive Inductive Logic Programming
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    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.
  110. Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition
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    John M. Zelle
    March 1993. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  111. Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition
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    Raymond J. Mooney
    Cognitive Science, 14(4):483-509, 1990.
  112. A General Explanation-Based Learning Mechanism and its Application to Narrative Understanding
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    Raymond J. Mooney
    Ph.D. thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, 1988
  113. Integrated Learning of Words and their Underlying Concepts
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    Raymond J. Mooney
    In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, 947-978, Seattle, WA, July 1987.
  114. Generalizing Explanations of Narratives into Schemata
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    Raymond J. Mooney
    In Proceedings of the Third International Machine Learning Workshop, 126--128, New Brunswick, New Jersey, 1985.
  115. Learning Schemata for Natural Language Processing
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    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.
  116. Generalizing Explanations of Narratives into Schemata
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    Raymond J. Mooney
    Masters Thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, 1985.