Probabilistic GLR Parsing: A New Formalization and Its Impact on Parsing Performance

Abstract

This paper presents a new formalization of probabilistic GLR (PGLR) language modeling for statistical parsing.Our model inherits its essential features from Briscoe and Carroll's generalized probabilistic LR model (Briscoe and Carroll 1993), which takes context of parse derivation into account by assigning a probability to each LR parsing action according to its left and right context. Briscoe and Carroll's model, however, has a drawback in that it is not formalized in any probabilistically well-founded way, which may degrade its parsing performance. Our formulation overcomes this drawback with a few significant refinements, while maintaining all the advantages of Briscoe and Carroll's modeling. In this paper, we discuss the formal and qualitative aspects of our PGLR model, illustrating the qualitative differences between Briscoe and Carroll's model and our model, and their expected impact on parsing performance.