Optimizing typed feature structure grammar parsing through non-statistical indexing

Abstract

This paper introduces an indexing method based on static analysis of grammar rules and type signatures for typed feature structure grammars (TFSGs). The static analysis tries to predict at compile-time which feature paths will cause unification failure during parsing at run-time. To support the static analysis, we introduce a new classification of the instances of variables used in TFSGs, based on what type of structure sharing they create. The indexing actions that can be performed during parsing are also enumerated. Non-statistical indexing has the advantage of not requiring training, and, as the evaluation using large-scale HPSGs demonstrates, the improvements are comparable with those of statistical optimizations. Such statistical optimizations rely on data collected during training, and their performance does not always compensate for the training costs.