Inferring Attribute Grammars with Structured Data for Natural Language Processing

This paper presents a method for inferring reversible attribute grammars from tagged natural language sentences. Attribute grammars are a form of augmented context free grammar that assign "meaning" in the form of a data structure to a string in a context free language. The method presented in this paper has the ability to infer attribute grammars that can generate a wide range of useful data structures such as simple and structured types, lists, concatenated strings, and natural numbers. The method also presents two new forms of grammar generalisation; generalisation based upon identification of optional phrases and generalisation based upon lists. The method has been applied to and tested on the task of the rapid development of spoken dialog systems.