tailieunhanh - Báo cáo khoa học: "AVM Description Compilation using Types as Modes"

This paper provides a method for generating compact and efficient code to implement the enforcement of a description in typed feature logic. It does so by viewing information about types through the course of code generation as modes of instantiation — a generalization of the common practice in logic programming of the hi nary instantiated/variable mode decl arations that advanced Prolog compilers use. Section 1 introduces the description language. Sections 2 and 3 motivate the view of mode and compilation taken here, and outline a mode declaration language for typed feature logic. Sections 4 through 7 then present the. | AVM Description Compilation using Types as Modes Gerald Penn Department of Computer Science University of Toronto gpenn@ Abstract This paper provides a method for generating compact and efficient code to implement the enforcement of a description in typed feature logic. It does so by viewing information about types through the course of code generation as modes of instantiation a generalization of the common practice in logic programming of the binary instantiated variable mode declarations that advanced Prolog compilers use. Section 1 introduces the description language. Sections 2 and 3 motivate the view of mode and compilation taken here and outline a mode declaration language for typed feature logic. Sections 4 through 7 then present the compiler. An evaluation on two grammars is presented at the end. 1 Descriptions The logic of typed feature structures Carpenter 1992 has been widely used as a means of formalizing and developing natural language grammars notably in Head-driven Phrase Structure Grammar Pollard and Sag 1994 . These grammars are stated using a vocabulary consisting of a finite meet semi-lattice of types and a set of features that must be specified for each grammar and this vocabulary must obey certain rules. A set of appropriateness conditions must specify for each feature which types of feature structures may bear it and which types of values it may take. Unique feature introduction states that every feature has a least type that bears it called its introducer. The effect of these rules is that typed feature structures TFSs can be described using a very terse description language. A TFS that matches the description NUMBER singular for example might implicitly be of a type index which introduces NUMBER. From that we can deduce that the TFS also bears values for PERSON and GENDER with particular appropriate values because those features are introduced by the same type. Terse descriptions allow us to work with very large TFSs .