Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
The theory of parsing, translation, and compiling
The theory of parsing, translation, and compiling
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
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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
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Probabilistic context-free grammars (PCFGs) are a popular cognitive model of syntax (Jurafsky, 1996). These can be formulated to be sensitive to human working memory constraints by application of a right-corner transform (Schuler, 2009). One side-effect of the transform is that it guarantees at most a single expansion (push) and at most a single reduction (pop) during a syntactic parse. The primary finding of this paper is that this property of right-corner parsing can be exploited to obtain a dramatic reduction in the number of random variables in a probabilistic sequence model parser. This yields a simpler structure that more closely resembles existing simple recurrent network models of sentence comprehension.