Compression By Induction of Hierarchical Grammars

Adaptive compression methods build models of symbol sequences. In many areas of computer science, models of sequences are constructed for their explanatory value. In contrast, data compression schemes use models that are opaque in that they do not provide descriptions of the sequence that can be understood or applied in other domains. Statistical methods that compress text well invariably generate large models that are not so much a structural description of the sequence as a record of frequencies of short substrings. Macro models replace repeated text by references to earlier occurrences and generally work within a small moving window of symbols so that any implicit model is transient. In both cases the model is flat and does not build up abstractions by combining references into higher level phrases.