Text Compression

By Paolo Ferragina, I. Nitto. 2009

312 citations recorded by the source corpus.

How researchers see this paper

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The Algorithm Design Manual (2020, background): “Also recommended is the older text by Bell, Cleary, and Witten [BCW90].”
A Machine Learning Perspective on Predictive Coding with PAQ8 (2012, background): “Table 1 shows a comparison of several compression algorithms on the Calgary corpus [10], a widely-used compression benchmark.”
Compressed full-text indexes (2007, background): “5.1. kth Order Empirical Entropy Opposed to the classical notion of kth order entropy [Bell et al. 1990], which can only be de.ned for in.nite sources, the kth-order empirical entropy de.ned by Manzini [2001] applies to .nite texts.”
Edinburgh Research Explorer Stanford University's Submissions to the WMT 2014 Translation Task (2014, methodology): “Finally, we estimated a 7-gram language model on the mapped corpus with SRILM (Stolcke, 2002) using Witten-Bell smoothing (Bell et al., 1990).”
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).”