Abstract

A major obstacle in developing efficient parsers for unification-based grammars is the slow parsing time caused by the complex and large structure used to represent grammatical categories. With the broadening coverage of such grammars, their size and complexity increases, creating the need for improved parsing techniques. Although several statistical optimizations exist today that exhibit significant improvements in parsing times, they rely on data collected during a training phase. This thesis presents a theoretical investigation of indexing based on static analysis of feature structure grammar rules, a method that has received little attention in the last few years in computational linguistics. This non-statistical approach has the advantage of not requiring training phases, although it is consistent with further statistical optimizations.