Text compression via alphabet re-representation

This article introduces the concept of alphabet re-representation in the co ntext of text compression. We consider re-representing the alphabet so that a representation of a character reflects its properties as a predictor of future rest. This enables us to use an estimator from a restricted class to map contexts to predictions of upcoming characters. We describe an algorit hm that uses this idea in conjunction with neural networks. The performance of our implementation is compared to other compression methods, such as UN IX compress, gzip, PPMC, and an alternative neural network approach. (C) 19 99 Elsevier Science Ltd. All rights reserved.