Probabilistic parsing and psychological plausibility
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
A generative constituent-context model for improved grammar induction
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
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
EEG responds to conceptual stimuli and corpus semantics
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Broad-coverage parsing using human-like memory constraints
Computational Linguistics
Syntactic and semantic factors in processing difficulty: an integrated measure
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
The influence of discourse on syntax a psycholinguistic model of sentence processing
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Syntactic and semantic factors in processing difficulty: an integrated measure
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Incremental, predictive parsing with psycholinguistically motivated tree-adjoining grammar
Computational Linguistics
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We pose the development of cognitively plausible models of human language processing as a challenge for computational linguistics. Existing models can only deal with isolated phenomena (e.g., garden paths) on small, specifically selected data sets. The challenge is to build models that integrate multiple aspects of human language processing at the syntactic, semantic, and discourse level. Like human language processing, these models should be incremental, predictive, broad coverage, and robust to noise. This challenge can only be met if standardized data sets and evaluation measures are developed.