Automatic Setting of Article Format Through Neural Networks

Abstract

The automatic format-setting of journal articles for reducing the workload of computer users involves two processes: automatic acquisition of article format and automatic recall of article formal. Several neural networks have been explored to implement the two processes. The advantages and disadvantages of these neural networks are evaluated in comparison with capabilities of conventional computer programs. A heteroassociative back-propagation network has been developed for the automatic acquisition process. This network excels over computer programs because of its abilities in learning and generalizing implicit knowledge from examples. A bidirectional associative memory network, a Boltzman network, and an autoassociative back-propagation network have been investigated for the automatic recall process. None of them excel over computer programs in terms of recall accuracy.