Integrating Statistical and Relational Learning for Semantic Parsing: Applications to Learning Natural Language Interfaces for Databases (2000)
The development of natural language interfaces (NLIs) for databases has been an interesting problem in natural language processing since the 70's. The need for NLIs has become more pronounced given the widespread access to complex databases now available through the Internet. However, such systems are difficult to build and must be tailored to each application. A current research topic involves using machine learning methods to automate the development of NLI's. This proposal presents a method for learning semantic parsers (systems for mapping natural language to logical form) that integrates logic-based and probabilistic methods in order to exploit the complementary strengths of these competing approaches. More precisely, an inductive logic programming (ILP) method, TABULATE, is developed for learning multiple models that are integrated via linear weighted combination to produce probabilistic models for statistical semantic parsing. Initial experimental results from three different domains suggest that an integration of statistical and logical approaches to semantic parsing can outperform a purely logical approach. Future research will further develop this integrated approach and demonstrate its ability to improve the automated development of NLI's.
unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.

Lappoon R. Tang Ph.D. Alumni ltang [at] utb edu