An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
An efficient context-free parsing algorithm
Communications of the ACM
Statistical Language Learning
Probabilistic top-down parsing and language modeling
Computational Linguistics
Prefix probabilities from stochastic Tree Adjoining Grammars
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A probabilistic earley parser as a psycholinguistic model
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Compositional expectation: a purely distributional model of compositional semantics
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Lexical surprisal as a general predictor of reading time
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
| Hi-index | 0.00 |
This paper investigates whether surprisal theory can account for differential processing difficulty in the NP-/S-coordination ambiguity in Dutch. Surprisal is estimated using a Probabilistic Context-Free Grammar (PCFG), which is induced from an automatically annotated corpus. We find that our lexicalized surprisal model can account for the reading time data from a classic experiment on this ambiguity by Frazier (1987). We argue that syntactic and lexical probabilities, as specified in a PCFG, are sufficient to account for what is commonly referred to as an NP-coordination preference.