TY  - JOUR
A1  - Demberg, Vera
A1  - Keller, Frank
A1  - Koller, Alexander
T1  - Incremental, Predictive Parsing with Psycholinguistically motivatedTree-adjoining grammar
T2  - Computational linguistics
N2  - 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). There is currently no broad-coverage parsing model with these properties, however. In this article, we present the first broad-coverage probabilistic parser for PLTAG, a variant of TAG that supports all three requirements. We train our 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. We also use our PLTAG model to predict human reading times, demonstrating a better fit on the Dundee eye-tracking corpus than a standard surprisal model.
Y1  - 2013
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/34563
SN  - 0891-2017
SN  - 1530-9312
VL  - 39
IS  - 4
SP  - 1025
EP  - 1066
PB  - MIT Press
CY  - Cambridge
ER  -