PMID: 12763317May 24, 2003Paper

Learning first-pass structural attachment preferences with dynamic grammars and recursive neural networks

Cognition
Patrick SturtPaolo Frasconi

Abstract

One of the central problems in the study of human language processing is ambiguity resolution: how do people resolve the extremely pervasive ambiguity of the language they encounter? One possible answer to this question is suggested by experience-based models, which claim that people typically resolve ambiguities in a way which has been successful in the past. In order to determine the course of action that has been "successful in the past" when faced with some ambiguity, it is necessary to generalize over past experience. In this paper, we will present a computational experience-based model, which learns to generalize over linguistic experience from exposure to syntactic structures in a corpus. The model is a hybrid system, which uses symbolic grammars to build and represent syntactic structures, and neural networks to rank these structures on the basis of its experience. We use a dynamic grammar, which provides a very tight correspondence between grammatical derivations and incremental processing, and recursive neural networks, which are able to deal with the complex hierarchical structures produced by the grammar. We demonstrate that the model reproduces a number of the structural preferences found in the experimental psycho...Continue Reading

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Citations

Aug 27, 2013·Neural Networks : the Official Journal of the International Neural Network Society·Franco ScarselliLucia Di Noi
Mar 11, 2006·IEEE Transactions on Neural Networks·Monica BianchiniFranco Scarselli
Aug 27, 2005·IEEE Transactions on Neural Networks·Fabrizio CostaGiovanni Soda
Nov 26, 2010·Experimental Psychology·Maaike LonckeTimothy Desmet
Nov 24, 2004·Neural Networks : the Official Journal of the International Neural Network Society·Barbara HammerMarc Strickert

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