Grammar-based Pre-processing for PPM

In this paper, we apply grammar-based pre-processing prior to using the Prediction by Partial Matching (PPM) compression algorithm. This achieves significantly better compression for different natural language texts compared to other well-known compression methods. Our method first generates a grammar based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in the text being compressed and then substitutes these sequences using the respective non-terminal symbols defined by the grammar in a pre-processing phase prior to the compression. This leads to significantly improved results in compression for various natural languages (a 5% improvement for American English, 10% for British English, 29% for Welsh, 10% for Arabic, 3% for Persian and 35% for Chinese). We describe further improvements using a two pass scheme where the grammar-based pre-processing is applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary Corpus and also achieve significantly improved results in compression, between 11% and 20%, when compared with other compression algorithms, including a grammar-based approach, the Sequitur algorithm.