The computational brain
Pulsed Neural Networks
Cell Assemblies as an Intermediate Level Model of Cognition
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Expressing generalizations in unification-based grammar formalisms
EACL '89 Proceedings of the fourth conference on European chapter of the Association for Computational Linguistics
Learning random walk models for inducing word dependency distributions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
Automatic WordNet mapping using word sense disambiguation
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
Neural Substrates of Action Event Knowledge
Journal of Cognitive Neuroscience
Using the web as an implicit training set: application to structural ambiguity resolution
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A cell assembly based model for the cortical microcircuitry
Neurocomputing
A cell assembly model for complex behaviour
Neurocomputing
Prepositional phrase attachment without oracles
Computational Linguistics
Processing with cell assemblies
Neurocomputing
A nearest-neighbor method for resolving PP-Attachment ambiguity
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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A neurocomputational model based on emergent massively overlapping neural cell assemblies (CAs) for resolving prepositional phrase (PP) attachment ambiguity is described. PP attachment ambiguity is a well-studied task in natural language processing and is a case where semantics is used to determine the syntactic structure. A large network of biologically plausible fatiguing leaky integrate-and-fire neurons is trained with semantic hierarchies (obtained from WordNet) on sentences with PP attachment ambiguity extracted from the Penn Treebank corpus. During training, overlapping CAs representing semantic similarities between the component words of the ambiguous sentences emerge and then act as categorizers for novel input. The resulting average resolution accuracy of 84.56% is on par with known machine learning algorithms.