Parsing with Psycholinguistically Motivated Tree-Adjoining Grammar

Abstract

Psycholinguistic research shows that key properties of the human
sentence processor are incrementality, connectedness (partial
structures contain no unattached nodes), and prediction (upcoming
syntactic structure is anticipated). However, there is currently no
broad-coverage parsing model with these properties. In this paper,
we present the first broad-coverage probabilistic parser for PLTAG,
a variant of TAG which supports all three requirements. We train the
parser on a TAG-transformed version of the Penn Treebank and show
that it achieves performance comparable to existing TAG
parsers that are incremental but not predictive.