A neural network for probabilistic information retrieval
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
Neurocomputing
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A neural algorithm for document clustering
Information Processing and Management: an International Journal - Special issue on parallel processing and information retrieval
Document classification and recurrent neural networks
CASCON '95 Proceedings of the 1995 conference of the Centre for Advanced Studies on Collaborative research
Using Kohonen maps to determine document similarity
CASCON '94 Proceedings of the 1994 conference of the Centre for Advanced Studies on Collaborative research
Recurrent-neural-network-based Boolean factor analysis and its application to word clustering
IEEE Transactions on Neural Networks
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In this paper we investigate the relevance of neural networks to the problem of document classification. We show that textual documents can be represented numerically in a semantically meaningful way, so that the back-propagation learning algorithm can be used to build a document classifying neural network. We show that the network can be taught to classify natural language text according to predefined specifications. The convergence properties of the prototype NeuroZ described in this paper make it clear that neural networks provide a new platform for the automatic classification of semantically similar documents and that a system can be built which distinguishes between relatively complex linguistic patterns.