Compressing semi-structured text using hierarchical phrase identification

Abstract

Many computer files contain highly-structured, predictable information interspersed with information which has less regularity and is therefore less predictable—such as free text. Examples range from word-processing source files, which contain precisely-expressed formatting specifications enclosing tracts of natural-language text, to files containing a sequence of filled-out forms which have a predefined skeleton clothed with relatively unpredictable entries. These represent extreme ends of a spectrum. Word-processing files are dominated by free text, and respond well to general-purpose compression techniques. Forms generally contain database-style information, and are most appropriately compressed by taking into account their special structure. But one frequently encounters intermediate cases. For example, in many email messages the formal header and the informal free-text content are equally voluminous. Short SGML files often contain comparable amounts of formal structure and informal text. Although such files may be compressed quite well by general-purpose adaptive text compression algorithms, which will soon pick up the regular structure during the course of normal adaptation, better compression can often be obtained by methods that are equipped to deal with both formal and informal structure.