# Natural Language Insensitive Short Textual String Compression

## Abstract

There are applications (such as Internet search engines) where short textual strings, for example abstracts or pieces of Web pages, need to be compressed independently of each other. The usual adaptive compression algorithms perform poorly on these short strings due to the lack of necessary data to learn. In this manuscript, we introduce a compression algorithm targeting short text strings; e.g., containing a few hundred symbols. We also target natural language insensitivity, to facilitate its robust compression and fast implementation. The algorithm is based on the following findings. Applying the move-to-front transform (MTFT) after the Burrows-Wheeler transform (BWT) brings the short textual strings to a "normalized form" where the distribution of the resulting "ranks" has a shape similar over the set of natural language strings. This facilitates the use of a static coding method with few variations, which we call shortBWT, where no on-line learning is needed, to encode the ranks. Finally, for short strings, shortBWT runs very fast because the strings fit into the cache of most current computers. The introduction for this paper will review the mathematical bases of BWT and MTFT, it will also review our recently published metric for rapidly pre-characterizing the compressibility of such short textual strings when using these transforms.