2026-09-19T15:41:58Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/81987 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

DeJong, Gerald F. Lake, John Michael 2015-09-25T20:21:18Z 2015-09-25T20:21:18Z 10000-01-01 2000 2000 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. Made available in DSpace on 2015-09-25T20:21:18Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9990052.pdf: 13518216 bytes, checksum: 5cdd5b829e74803f652e67dd74bf539c (MD5) Previous issue date: 2000 Embargo set by: Seth Robbins for item 83268 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 196 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2000. http://hdl.handle.net/2142/81987 (MiAaPQ)AAI9990052 eng Computer Science Sequentialized Language Models text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.