@inproceedings{bentz-2023-zipfian,
    title = "The {Z}ipfian Challenge: Learning the statistical fingerprint of natural languages",
    author = "Bentz, Christian",
    editor = "Jiang, Jing  and
      Reitter, David  and
      Deng, Shumin",
    booktitle = "Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.conll-1.3/",
    doi = "10.18653/v1/2023.conll-1.3",
    pages = "27--37",
    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."
}