An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
A neural probabilistic language model
The Journal of Machine Learning Research
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Probabilistic top-down parsing and language modeling
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
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Three new graphical models for statistical language modelling
Proceedings of the 24th international conference on Machine learning
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
CMCL '10 Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics
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Probabilistic accounts of language processing can be psychologically tested by comparing word-reading times (RT) to the conditional word probabilities estimated by language models. Using surprisal as a linking function, a significant correlation between unlexicalized surprisal and RT has been reported (e.g., Demberg and Keller, 2008), but success using lexicalized models has been limited. In this study, phrase structure grammars and recurrent neural networks estimated both lexicalized and unlexicalized surprisal for words of independent sentences from narrative sources. These same sentences were used as stimuli in a self-paced reading experiment to obtain RTs. The results show that lexicalized surprisal according to both models is a significant predictor of RT, outperforming its un-lexicalized counterparts.