Compressing semi-structured text using hierarchical phrase identifications

This paper takes a compression scheme that infers a hierarchical grammar from its input, and investigates its application to semi-structured text. Although there is a huge range and variety of data that comes within the ambit of "semi-structured", we focus attention on a particular, and very large, example of such text. Consequently the work is a case study of the application of grammar-based compression to a large-scale problem. We begin by identifying some characteristics of semi-structured text that have special relevance to data compression. We then give a brief account of a particular large textual database, and describe a compression scheme that exploits its structure. In addition to providing compression, the system gives some insight into the structure of the database. Finally we show how the hierarchical grammar can be generalized, first manually and then automatically, to yield further improvements in compression performance.