UT ML Group: Learning for Semantic Parsing

Semantic parsing is the process of mapping a natural-language sentence into a formal representation of its meaning. A shallow form of semantic representation is a case-role analysis (a.k.a. a semantic role labeling), which identifies roles such as agent, patient, source, and destination. A deeper semantic analysis provides a representation of the sentence in predicate logic or other formal language which supports automated reasoning. We have developed methods for automatically learning semantic parsers from annotated corpora using inductive logic programming and other learning methods. We have explored learning semantic parsers for mapping natural-language sentences to case-role analyses, formal database queries, and formal command languages (i.e. the Robocup coaching language for use in advice-taking learners ). We have also explored methods for learning semantic lexicons, i.e. databases of words or phrases paired with one or more alternative formal meaning representations. Semantic lexicons can also be learned from semantically annotated sentences and are an important source of knowledge for semantic parsing. Learning for semantic parsing is part of our research on natural language learning.

"The fish trap exists because of the fish. Once you've gotten the fish you can forget the trap. The rabbit snare exists because of the rabbit. Once you've gotten the rabbit, you can forget the snare. Words exist because of meaning. Once you've gotten the meaning, you can forget the words. Where can I find a man who has forgotten words so I can talk with him?"
-- The Writings of Chuang Tzu, 4th century B.C. (Original text in Chinese)

Demos of learning natural-language interfaces:


Publications

  1. Learning to Sportscast: A Test of Grounded Language Acquisition [Abstract] [PDF]
    David L. Chen and Raymond J. Mooney
    In Proceedings of the 25th International Conference on Machine Learning (ICML) , Helsinki, Finland, July 2008.

  2. Learning for Semantic Parsing with Kernels under Various Forms of Supervision [Abstract] [PDF]
    Rohit J. Kate
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August 2007.
    159 pages.

  3. Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques [Abstract] [PDF]
    Yuk Wah Wong
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August 2007.
    188 pages.
    Also appears as Technical Report AI07-343, Artificial Intelligence Lab, University of Texas at Austin, August 2007.

  4. Learning Language Semantics from Ambiguous Supervision [Abstract] [PDF]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-2007), Vancouver, BC, pp. 895-900, July 2007.

  5. Learning Synchronous Grammars for Semantic Parsing with Lambda Calculus [Abstract] [PDF]
    Yuk Wah Wong and Raymond J. Mooney
    Best Paper Award
    In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-2007), pp. 960-967, Prague, Czech Republic, June 2007.

  6. Semi-Supervised Learning for Semantic Parsing using Support Vector Machines [Abstract] [PDF]
    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), pp. 81-84, Rochester, NY, April 2007.

  7. Generation by Inverting a Semantic Parser That Uses Statistical Machine Translation [Abstract] [PDF]
    Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (NAACL/HLT-2007), pp. 172-179, Rochester, NY, April 2007.

  8. Learning for Semantic Parsing [Abstract] [PDF]
    Raymond J. Mooney
    Computational Linguistics and Intelligent Text Processing: Proceedings of the 8th International Conference, CICLing 2007, Mexico City (invited paper), A. Gelbukh (Ed.), pp. 311-324, Springer, Berlin, Germany, February 2007.

  9. Using String-Kernels for Learning Semantic Parsers [Abstract] [PDF]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the Joint 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL-2006), pp. 913-920, Sydney, Australia, July 2006.

  10. Discriminative Reranking for Semantic Parsing [Abstract] [PDF]
    Ruifang Ge and Raymond J. Mooney
    In Proceedings of the COLING/ACL-2006 Main Conference Poster Sessions, pp. 263-270, Sydney, Australia, July 2006.

  11. Learning for Semantic Parsing with Statistical Machine Translation [Abstract] [PDF]
    Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL-2006), pp. 439-446, New York City, NY, June 2006.

  12. Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques [Abstract] [PDF]
    Ruifang Ge
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, February 2006.
    41 pages.
    Also appears as Technical Report UT-AI-TR-06-327, Artificial Intelligence Lab, University of Texas at Austin, February 2006.

  13. A Kernel-based Approach to Learning Semantic Parsers [Abstract] [PDF]
    Rohit J. Kate
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, November 2005.
    34 pages.
    Also appears as Technical Report UT-AI-05-326, Artificial Intelligence Lab, University of Texas at Austin, November 2005.

  14. Learning for Semantic Parsing Using Statistical Machine Translation Techniques [Abstract] [PDF]
    Yuk Wah Wong
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, October 2005.
    53 pages.
    Also appears as Technical Report UT-AI-05-323, Artificial Intelligence Lab, University of Texas at Austin, October 2005.

  15. A Statistical Semantic Parser that Integrates Syntax and Semantics [Abstract] [PDF]
    Ge, R. and Mooney, R.J.
    Proceedings of the Ninth Conference on Computational Natural Language Learning, Ann Arbor, MI, pp. 9--16, June 2005.

  16. Learning to Transform Natural to Formal Languages [Abstract] [PDF]
    Kate, R.J., Wong, Y. W., and Mooney, R.J.
    Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), Pittsburgh, PA, pp. 1062--1068, July 2005.

  17. Learning Transformation Rules for Semantic Parsing [Abstract] [PDF]
    Rohit J. Kate, Yuk Wah Wong, Ruifang Ge, and Raymond J. Mooney
    Unpublished Technical Note, April 2004.

  18. Learning Semantic Parsers: An Important But Under-Studied Problem [Abstract] [PDF]
    Raymond J. Mooney
    Papers from the AAAI 2004 Spring Symposium on Language Learning: An Interdisciplinary Perspective, pp. 39-44, Stanford, CA, March 2004.

  19. Integrating Top-down and Bottom-up Approaches in Inductive Logic Programming: Applications in Natural Language Processing and Relational Data Mining [Abstract] [PDF]
    Lappoon R. Tang
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August 2003.
    219 pages

  20. Acquiring Word-Meaning Mappings for Natural Language Interfaces [Abstract] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    Journal of Artificial Intelligence Research, 18, (2003) pp. 1-44.

  21. Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing [Abstract] [PDF]
    Lappoon R. Tang and Raymond J. Mooney
    Proceedings of the 12th European Conference on Machine Learning (ECML-2001), pp. 466-477, Freiburg, Germany, September 2001.

  22. Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing [Abstract] [PDF]
    Lappoon R. Tang and Raymond J. Mooney
    Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000), pp. 133 - 141, Hong Kong, October 2000

  23. Integrating Statistical and Relational Learning for Semantic Parsing: Applications to Learning Natural Language Interfaces for Databases [Abstract] [PDF]
    Lappoon R. Tang
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, May 2000.
    50 pages

  24. Automatic Construction of Semantic Lexicons for Learning Natural Language Interfaces [Abstract] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), Orlando, FL, pp. 487-493, July 1999.

  25. Learning for Semantic Interpretation: Scaling Up Without Dumbing Down [Abstract] [PDF]
    Raymond J. Mooney
    Workshop Notes for the Workshop on Learning Language in Logic Bled, Slovenia, pp. 7-14, June 1999.
    Also appears in Learning Language in Logic, J. Cussens and S. Dzeroski (Eds.), pp. 57-66, Springer Verlag, Berlin, 2000.

  26. Active Learning for Natural Language Parsing and Information Extraction [Abstract] [PDF]
    Cynthia A. Thompson, Mary Elaine Califf and Raymond J. Mooney
    Nominated for Best Paper Award
    Proceedings of the Sixteenth International Machine Learning Conference (ICML-99) , Bled, Slovenia, pp. 406-414, June 1999.

  27. Semantic Lexicon Acquisition for Learning Natural Language Interfaces [Abstract] [PDF]
    Cynthia Ann Thompson
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, December 1998.
    101 pages.
    Also appears as Technical Report AI 99-278, Artificial Intelligence Lab, University of Texas at Austin.

  28. Semantic Lexicon Acquisition for Learning Natural Language Interfaces [Abstract] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    Proceedings of the Sixth Workshop on Very Large Corpora, pp. 57-65, Montreal, Quebec, Canada, August 1998.
    TR AI 98-273, Artificial Intelligence Lab, University of Texas at Austin, May 1998.

  29. Learning to Parse Natural Language Database Queries into Logical Form [Abstract] [PDF]
    Cynthia A. Thompson Raymond J. Mooney, and Lappoon R. Tang
    Proceedings of the ML-97 Workshop on Automata Induction, Grammatical Inference, and Language Acquisition, Nashville, TN, July 1997.

  30. An Inductive Logic Programming Method for Corpus-based Parser Construction [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Unpublished Technical Note, January 1997

  31. Semantic Lexicon Acquisition for Learning Parsers [Abstract] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    Unpublished Technical Note, January 1997

  32. Inductive Logic Programming for Natural Language Processing [Abstract] [PDF]
    Raymond J. Mooney
    Inductive Logic Programming: Selected Papers from the 6th International Workshop, S. Muggleton (Ed.), pp.3-22, Springer Verlag, Berlin, 1997.
    Also appears in Proceedings of the 6th International Inductive Logic Programming Workshop, pp. 205-224, Stockholm, Sweden, August 1996.

  33. Learning to Parse Database Queries using Inductive Logic Programming [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 1050-1055, Portland, OR, August 1996.

  34. Corpus-Based Lexical Acquisition For Semantic Parsing [Abstract] [PDF]
    Cynthia Thompson
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, February 1996.
    38 pages

  35. Lexical Acquisition: A Novel Machine Learning Problem [Abstract] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    Technical Report, Artificial Intelligence Lab, University of Texas at Austin, January 1996.

  36. Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers [Abstract] [PDF]
    John M. Zelle
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August 1995.
    204 pages
    Also appears as Technical Report AI 96-249, Department of Computer Sciences, University of Texas at Austin

  37. A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Working Notes of the IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing, pp.79-86, Montreal, Quebec, Canada, August 1995.

  38. Acquisition of a Lexicon from Semantic Representations of Sentences [Abstract] [PDF]
    Cynthia A. Thompson
    Proceedings of the 33rd Annual Meeting of the Association of Computational Linguistics (ACL-95), pp. 335-337, Boston, MA, July 1995.

  39. Learning Semantic Grammars With Constructive Inductive Logic Programming [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Eleventh National Conference of the American Association for Artificial Intelligence (AAAI-93), pp. 817-822, Washington, D.C. July 1993.

  40. Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition [Abstract] [PDF]
    John M. Zelle
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, March 1993.
    36 pages

mooney@cs.utexas.edu