Automated Construction of Database Interfaces: Intergrating Statistical and Relational Learning for Semantic Parsing

Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing Lappoon R. Tang and Raymond J. Mooney Department of Computer Sciences University of Texas at Austin Austin, TX 78712-1188 {rupert, mooney}@cs, utexas, edu Abstract The development of natural language inter- faces (NLI's) for databases has been a chal- lenging problem in natural language process- ing (NLP) since the 1970's. The need for NLI's has become more pronounced due to the widespread access to complex databases now available through the Internet. A challenging problem for empirical NLP is the automated acquisition of NLI's from training examples. We present a method for integrating statisti- cal and relational learning techniques for this task which exploits the strength of both ap- proaches. Experimental results from three dif- ferent domains suggest that such an approach is more robust than a previous purely logic- based approach. 1 Introduction We use the term semantic parsing to refer to the process of mapping a natural language sentence to a structured meaning representa- tion. One interesting application of semantic parsing is building natural language interfaces for online databases. The need for such appli- cations is growing since when information is delivered through the Internet, most users do not know the underlying database access lan- guage. An example of such an interface that we have developed is shown in Figure 1. Traditional (rationalist) approaches to con- structing database interfaces require an ex- pert to hand-craft an appropriate semantic parser (Woods, 1970; Hendrix et al., 1978). However, such hand-crafted parsers are time consllming to develop and suffer from prob- lems with robustness and incompleteness even for domain specific applications. Neverthe- less, very little research in empirical NLP has explored the task of automatically acquiring such interfaces from annotated training ex- amples. The only exceptions of which we are aware axe a statistical approach to map- ping airline-information queries into SQL pre- sented in (Miller et al., 1996), a probabilistic decision-tree method for the same task de- scribed in (Kuhn and De Mori, 1995), and an approach using relational learning (a.k.a. inductive logic programming, ILP) to learn a logic-based semantic parser described in (Zelle and Mooney, 1996). The existing empirical systems for this task employ either a purely logical or purely sta- tistical approach. The former uses a deter- ministic parser, which can suffer from some of the same robustness problems as rational- ist methods. The latter constructs a prob- abilistic grammar, which requires supplying a sytactic parse tree as well as a semantic representation for each training sentence, and requires hand-crafting a small set of contex- tual features on which to condition the pa- rameters of the model. Combining relational and statistical approaches can overcome the need to supply parse-trees and hand-crafted features while retaining the robustness of sta- tistical parsing. The current work is based on the CHILL logic-based parser-acquisition framework (Zelle and Mooney, 1996), retain- ing access to the complete parse state for mak- ing decisions, but building a probabilistic re- lational model that allows for …

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