Grammar compression with probabilistic context-free grammar

Hiroaki Naganuma, Hendrian Diptarama, Ryo Yoshinaka, Ayumi Shinohara, Naoki Kobayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - DCC 2020
Subtitle of host publicationData Compression Conference
EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781728164571
DOIs
Publication statusPublished - 2020 Mar
Event2020 Data Compression Conference, DCC 2020 - Snowbird, United States
Duration: 2020 Mar 242020 Mar 27

Publication series

NameData Compression Conference Proceedings
Volume2020-March
ISSN (Print)1068-0314

Conference

Conference2020 Data Compression Conference, DCC 2020
CountryUnited States
CitySnowbird
Period20/3/2420/3/27

Keywords

  • Entropy compression
  • Grammar compression
  • Probabilistic context free grammar
  • Straight line grammar

ASJC Scopus subject areas

  • Computer Networks and Communications

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