Lappoon R. Tang
Machine Learning Lab: Ph.D. Alumni
Neural Networks Lab: Undergraduate Alumni
Lappoon's research focuses on machine learning of natural language. As an undergraduate with the NNRG he worked on learning word representations from text corpora. He completed a PhD with the Machine Learning group on inductive logic programming applied to natural language processing and relational data mining.
Homepage:
http://blue.utb.edu/ltang/
Publications
     [Expand to show all 11][Minimize]
Relational Data Mining with Inductive Logic Programming for Link Discovery 2004
Raymond J. Mooney, P. Melville, L. R. Tang, J. Shavlik, I. Dutra and D. Page, Data Mining: Next Generation Challenges and Future DirectionsKargupta, H., Joshi, A., Sivakumar K., and Yesha, Y. (Eds.) (2004), pp. 239--254. AAAI Press.
Integrating Top-down and Bottom-up Approaches in Inductive Logic Programming: Applications in Natural Language Processing and Relational Data Mining 2003
Lappoon R. Tang, PhD Thesis, Department of Computer Sciences, University of Texas.
Scaling Up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism 2003
Lappoon R. Tang, Raymond J. Mooney, and Prem Melville, In Proceedings of the KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), pp. 107--121, Washington DC, August 2003.
Relational Data Mining with Inductive Logic Programming for Link Discovery 2002
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.
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing 2001
Lappoon R. Tang and Raymond J. Mooney, In Proceedings of the 12th European Conference on Machine Learning, pp. 466-477, Freiburg, Germany 2001.
Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing 2000
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), pp. 133-141, Hong Kong, October 2000.
Integrating Statistical and Relational Learning for Semantic Parsing: Applications to Learning Natural Language Interfaces for Databases 2000
Lappoon R. Tang, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Nested Expressions in Logic Programs 1999
Vladimir Lifschitz, Lappoon R. Tang and Hudson Turner, Annals of Mathematics and Artificial Intelligence, Vol. 25 (1999), pp. 369-389.
An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions 1998
Lappoon R. Tang, Mary Elaine Califf, and Raymond J. Mooney, Technical Report AI 98-271, Artificial Intelligence Lab, University of Texas at Austin.
Learning to Parse Natural Language Database Queries into Logical Form 1997
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.
A Connectionist Corpus-Based Approach to the Building of Word Representations 1994
Rupert L. Tang, Technical Report HR-94-01, Department of Computer Science, The University of Texas at Austin.
Projects
Learning Word Meanings: The FGREP Method 1987 - 1994
Areas of Interest
Concept and Schema Learning Natural Language Processing (Cognitive)
Software/Data
FGREPNET The FGREPNET package contains the C-code and data for training and testing an FGREP network in developing distributed re... 1994
Labs
Formerly affiliated with Machine Learning Formerly affiliated with Neural Networks