Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Theory of Syntactic Recognition for Natural Languages
Theory of Syntactic Recognition for Natural Languages
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Probabilistic parsing and psychological plausibility
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
An efficient implementation of a new DOP model
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Inducing history representations for broad coverage statistical parsing
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Deterministic left corner parsing
SWAT '70 Proceedings of the 11th Annual Symposium on Switching and Automata Theory (swat 1970)
Positive results for parsing with a bounded stack using a model-based right-corner transform
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Broad-coverage parsing using human-like memory constraints
Computational Linguistics
HHMM parsing with limited parallelism
CMCL '10 Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics
Incremental syntactic language models for phrase-based translation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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To support incremental interpretation, any model of human sentence processing must not only process the sentence incrementally, it must to some degree restrict the number of analyses which it produces for any sentence prefix. Deterministic parsing takes the extreme position that there can only be one analysis for any sentence prefix. Experiments with an incremental statistical parser show that performance is severely degraded when the search for the most probable parse is pruned to only the most probable analysis after each prefix. One method which has been extensively used to address the difficulty of deterministic parsing is lookahead, where information about a bounded number of subsequent words is used to decide which analyses to pursue. We simulate the effects of lookahead by summing probabilities over possible parses for the lookahead words and using this sum to choose which parse to pursue. We find that a large improvement is achieved with one word lookahead, but that more lookahead results in relatively small additional improvements. This suggests that one word lookahead is sufficient, but that other modifications to our left-corner parsing model could make deterministic parsing more effective.