An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
An efficient context-free parsing algorithm
Communications of the ACM
Entropy rate constancy in text
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Variation of entropy and parse trees of sentences as a function of the sentence number
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A noisy-channel model of rational human sentence comprehension under uncertain input
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Human sentence processing involves integrating probabilistic knowledge from a variety of sources in order to incrementally determine the hierarchical structure for the serial input stream. While a large number of sentence processing effects have been explained in terms of comprehenders' rational use of probabilistic information, effects of local coherences have not. We present here a new model of local coherences, viewing them as resulting from a belief-update process, and show that the relevant probabilities in our model are calculable from a probabilistic Earley parser. Finally, we demonstrate empirically that an implemented version of the model makes the correct predictions for the materials from the original experiment demonstrating local coherence effects.