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        <title>The Zipfian Challenge: Learning the statistical fingerprint of natural languages</title>
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        <namePart type="given">Christian</namePart>
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            <title>Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)</title>
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            <namePart type="given">Jing</namePart>
            <namePart type="family">Jiang</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Reitter</namePart>
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            <namePart type="given">Shumin</namePart>
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    <abstract>Human languages are often claimed to fundamentally differ from other communication systems. But what is it exactly that unites them as a separate category? This article proposes to approach this problem – here termed the Zipfian Challenge – as a standard classification task. A corpus with textual material from diverse writing systems and languages, as well as other symbolic and non-symbolic systems, is provided. These are subsequently used to train and test binary classification algorithms, assigning labels “writing” and “non-writing” to character strings of the test sets. The performance is generally high, reaching 98% accuracy for the best algorithms. Human languages emerge to have a statistical fingerprint: large unit inventories, high entropy, and few repetitions of adjacent units. This fingerprint can be used to tease them apart from other symbolic and non-symbolic systems.</abstract>
    <identifier type="citekey">bentz-2023-zipfian</identifier>
    <identifier type="doi">10.18653/v1/2023.conll-1.3</identifier>
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        <url>https://aclanthology.org/2023.conll-1.3</url>
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        <date>2023-12</date>
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            <start>27</start>
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%0 Conference Proceedings
%T The Zipfian Challenge: Learning the statistical fingerprint of natural languages
%A Bentz, Christian
%Y Jiang, Jing
%Y Reitter, David
%Y Deng, Shumin
%S Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F bentz-2023-zipfian
%X Human languages are often claimed to fundamentally differ from other communication systems. But what is it exactly that unites them as a separate category? This article proposes to approach this problem – here termed the Zipfian Challenge – as a standard classification task. A corpus with textual material from diverse writing systems and languages, as well as other symbolic and non-symbolic systems, is provided. These are subsequently used to train and test binary classification algorithms, assigning labels “writing” and “non-writing” to character strings of the test sets. The performance is generally high, reaching 98% accuracy for the best algorithms. Human languages emerge to have a statistical fingerprint: large unit inventories, high entropy, and few repetitions of adjacent units. This fingerprint can be used to tease them apart from other symbolic and non-symbolic systems.
%R 10.18653/v1/2023.conll-1.3
%U https://aclanthology.org/2023.conll-1.3
%U https://doi.org/10.18653/v1/2023.conll-1.3
%P 27-37

Markdown (Informal)

[The Zipfian Challenge: Learning the statistical fingerprint of natural languages](https://aclanthology.org/2023.conll-1.3) (Bentz, CoNLL 2023)

• The Zipfian Challenge: Learning the statistical fingerprint of natural languages (Bentz, CoNLL 2023)

ACL

• Christian Bentz. 2023. The Zipfian Challenge: Learning the statistical fingerprint of natural languages. In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 27–37, Singapore. Association for Computational Linguistics.