Grammar compression with probabilistic context-free grammar

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - DCC 2020
ホスト出版物のサブタイトルData Compression Conference
編集者Ali Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
出版社Institute of Electrical and Electronics Engineers Inc.
ページ386
ページ数1
ISBN(電子版)9781728164571
DOI
出版ステータスPublished - 2020 3
イベント2020 Data Compression Conference, DCC 2020 - Snowbird, United States
継続期間: 2020 3 242020 3 27

出版物シリーズ

名前Data Compression Conference Proceedings
2020-March
ISSN(印刷版)1068-0314

Conference

Conference2020 Data Compression Conference, DCC 2020
国/地域United States
CitySnowbird
Period20/3/2420/3/27

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信

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