Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409154 | Neurocomputing | 2008 | 11 Pages |
Abstract
The performance of a simple recurrent neural network on the implicit acquisition of a context-free grammar is re-examined and found to be significantly higher than previously reported by Elman. This result is obtained although the previous work employed a multilayer extension of the basic form of simple recurrent network and restricted the complexity of training and test corpora. The high performance is traced to a well-organized internal representation of the grammatical elements, as probed by a principal-component analysis of the hidden-layer activities. From the next-symbol-prediction performance on sentences not present in the training corpus, a capacity of generalization is demonstrated.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Bo Cartling,