Grammar Compression with Probabilistic Context-Free Grammar

We propose a new approach for universal lossless text compression, based on grammar compression. In the literature, a target string T has been compressed as a context-free grammar G in Chomsky normal form satisfying L(G) = T. Such a grammar is often called a straight-line program (SLP). In this paper, we consider a probabilistic grammar G that generates T, but not necessarily as a unique element of L(G). In order to recover the original text T unambiguously, we keep both the grammar G and the derivation tree of T from the start symbol in G, in compressed form. We show some simple evidence that our proposal is indeed more efficient than SLPs for certain texts, both from theoretical and practical points of view.