Toward a psycholinguistically-motivated model of language processing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
The information-processing difficulty of incremental parsing
IncrementParsing '04 Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
Complexity metrics in an incremental right-corner parser
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
| Hi-index | 0.00 |
Toward a probabilistic theory of human sentence processing, this dissertation proposes a definition of computational work done in the course of analyzing sentences generated by formal grammars. It applies the idea of entropy from information theory to the set of derivations compatible with an initial substring of a sentence. Given a probabilistic grammar, this permits the set of such compatible derivations to be viewed as a random variable, and the change in uncertainty about the outcomes to be calculated. This definition of computational work is examined as a cognitive model of human sentence processing difficulty. To apply the model, a variety of existing syntactic proposals for English sentences are cast as probabilistic Generalized Phrase Structure Grammars (Gazdar et al., 1985) and probabilistic Minimalist Grammars (Stabler, 1997). It is shown that the amount of predicted processing effort in relative clauses correlates with the Accessibility Hierarchy of relativized grammatical relations (Keenan and Comrie, 1977) on a Kaynian (1994) view of relative clause structure. Results from three new on-line sentence reading experiments suggest that while genitivity has the role suggested by the Accessibility Hierarchy, extraction from oblique does not. Evidence is also found for a direct object/indirect object processing asymmetry, which can be derived from the proposed cognitive model under the assumption of a lexicalized probabilistic grammar.