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2014 | 23 |
Article title

Hilberg’s Conjecture – a Challenge for Machine Learning

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PL
Abstracts
PL
We review three mathematical developments linked with Hilberg’s conjecture – a hypothesis about the power-law growth of entropy of texts in natural language, which sets up a challenge for machine learning. First, considerations concerning maximal repetition indicate that universal codes such as the Lempel-Ziv code may fail to efficiently compress sources that satisfy Hilberg’s conjecture. Second, Hilberg’s conjecture implies the empirically observed power-law growth of vocabulary in texts. Third, Hilberg’s conjecture can be explained by a hypothesis that texts describe consistently an infinite random object.
Publisher
Year
Volume
23
Physical description
Dates
published
2014
online
21 - 05 - 2015
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Publication order reference
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YADDA identifier
bwmeta1.element.ojs-issn-2083-8476-year-2014-volume-23-article-2201
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