A corpus for the evaluation of lossless compression algorithms

By Ross Arnold, T. Bell. 1997

How researchers see this paper

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Automated Curriculum Learning for Neural Networks (2017, methodology): “We trained character-level Kneser-Ney n-gram models (Kneser and Ney, 1995) on the King James Bible data from the Canterbury corpus (Arnold and Bell, 1997), with the maximum depth parameter n ranging between 0 to 10 .”
Energy-aware lossless data compression (2006, background): “Though more modem and/or, methodically-chosen corpora exist, compression ratios for a given compressor have remained nearly identical over a range of well-chosen input datasets [3].”
On Prediction Using Variable Order Markov Models (2004, background): “Two examples are the Canterbury Corpus and the ‘Large Canterbury Corpus’ (Arnold & Bell, 1997) and the Silesia Compression Corpus (Deorowicz, 2003), which contains significantly larger files than both the Calgary and Canterbury corpora.26 Bunton (1997) provides a (Calgary Corpus) comparison between ”
Online Learning with Gated Linear Networks (2017, methodology): “At the time of writing, context mixing variants achieve the best compression ratios on widely used benchmarks such as the Calgary Corpus (Bell et al., 1990), Canterbury Corpus (Bell and Arnold, 1997), and Wikipedia (Hutter, 2017).”