Natural language parsing as statistical pattern recognition
Natural language parsing as statistical pattern recognition
The syntactic process
Towards efficient statistical parsing using lexicalized grammatical information
Towards efficient statistical parsing using lexicalized grammatical information
Probabilistic top-down parsing and language modeling
Computational Linguistics
Supertagging: an approach to almost parsing
Computational Linguistics
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Feature structures based Tree Adjoining Grammars
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 2
Left-corner parsing and psychological plausibility
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Probabilistic tree-adjoining grammar as a framework for statistical natural language processing
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
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
Statistical parsing with an automatically-extracted tree adjoining grammar
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
A uniform method of grammar extraction and its applications
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Incremental parsing with the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Computational Linguistics
Stochastically evaluating the validity of partial parse trees in incremental parsing
IncrementParsing '04 Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
Incrementality in deterministic dependency parsing
IncrementParsing '04 Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Broad-coverage parsing using human-like memory constraints
Computational Linguistics
Journal of Computer and System Sciences
Syntactic and semantic factors in processing difficulty: an integrated measure
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Complexity metrics in an incremental right-corner parser
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Cognitively plausible models of human language processing
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
IEEE Transactions on Information Theory
Sequential vs. hierarchical syntactic models of human incremental sentence processing
CMCL '12 Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics
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Psycholinguistic research shows that key properties of the human sentence processor are incrementality, connectedness partial structures contain no unattached nodes, and prediction upcoming syntactic structure is anticipated. There is currently no broad-coverage parsing model with these properties, however. In this article, we present the first broad-coverage probabilistic parser for PLTAG, a variant of TAG that supports all three requirements. We train our parser on a TAG-transformed version of the Penn Treebank and show that it achieves performance comparable to existing TAG parsers that are incremental but not predictive. We also use our PLTAG model to predict human reading times, demonstrating a better fit on the Dundee eye-tracking corpus than a standard surprisal model.