INDEXING METHODS FOR EFFICIENT PARSING WITH TYPED FEATURE STRUCTURE GRAMMARS

摘要

Recent years have witnessed an increase in the use of unification-based grammars, especially of typed feature structure grammars (TFSGs). A major obstacle in developing efficient parsers for unification-based grammars (UBGs) is the slow parsing time caused by the large amount of data and the complex structure used to represent grammatical entities. With the broadening coverage of such grammars, their size and complexity increases, rendering the need for improved parsing techniques more acute. Although several methods exist today that exhibits significant improvements in parsing times, most of them rely on statistical data collected during training phases. Our goal is to obtain an indexing method that produces improvements comparable to those of statistical methods, but without lengthy training processes. In this paper, we present an indexing technique based on static analysis of the grammar rules, a method that has received little attention in the last few years in computational linguistics. This method has the advantage of not requiring training phases, and, as experimental results show, it offers significant improvements of parsing times for typed feature structure (TFS) grammars.