Fast Text Compression with Neural Networks

May 2000

Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2000)

Volume

Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2000)

All Papers

Neural networks have the potential to extend data com-pression algorithms beyond the character level n-gram models now in use, but have usually been avoided because they are too slow to be practical. We introduce a model that produces better compression than popular Limpel-Ziv compressors (zip, gzip, compress), and is competitive in time, space, and compression ratio with PPM and Burrows-Wheeler algorithms, currently the best known. The compressor, a bit-level predictive arithmetic encoder is fast because only 4-5 connections are simultaneously active and because it uses a variable learning rate optimized for one-pass training.