Incremental, Predictive Parsing with Psycholinguistically Motivated Tree-Adjoining Grammar

Vera Demberg, Frank Keller, Alexander Koller
2013 Computational Linguistics  
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 article, we present the first broad-coverage probabilistic parser for PLTAG, a variant of TAG which supports all three requirements. We train our parser on a TAG-transformed
more » ... AG-transformed version of the Penn Treebank and show that it achieves performance comparable to existing TAG parsers that are incremental but not predictive. We also use our PLTAG model to predict human reading times, demonstrating a better fit on the Dundee eyetracking corpus than a standard surprisal model.
doi:10.1162/coli_a_00160 fatcat:njvew723djeaja3udn5icccu5y