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https://hdl.handle.net/2142/81987
Description
Title
Sequentialized Language Models
Author(s)
Lake, John Michael
Issue Date
2000
Doctoral Committee Chair(s)
DeJong, Gerald F.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
We then turn to construction of a sequentialized grammatical model of linguistic objects in text compression. We develop the Prediction by Grammatical Match technique, a new compression framework employing a static context-free grammar and an adaptive finite-context statistical model. These compressors are adaptive, general compressors that operate in linear time and bounded space. We show these compressors can deliver substantial reductions in both bits-per-character rates and space usage, and suffer almost no penalty when the grammar does not apply. The new technique rests on three primary technical innovations: an algorithm for designing an optimal, strictly bottom-up parseable metalanguage for a compression scheme comprising multiple grammars; a principled approach to ambiguity and agrammatical text; and an incremental analysis selection algorithm. The metalanguage construction emphasizes lexical left-corner analysis descriptions, with each symbol in a description representing a maximal bundle of bottom-up and top-down information by naming the production introducing the next lexical left-corner item. These three innovations combine into a very powerful compression system that solves an important, long standing problem: efficient and effective use of context-free grammars in general data compression.
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