Improving the classification accuracy of automatic text processing systems using context vectors and back-propagation algorithms

We analyze some of the benefits of combining the context-vector representation of documents with the back-propagation paradigm for document classification. We discuss an implementation of this architecture, called NeuroFile, which combines automatic document classification with similarity-based, as well as Boolean retrieval facilities in a single electronic filing system. The quality of performance of NeuroFile is compared with an earlier system called NeuroClass. We show that NeuroFile achieves a 9% classification improvement over NeuroClass.