@inproceedings{tiwari-2026-statistical,
    title = "Statistical Structure in {I}ndus Sign Sequences",
    author = "Tiwari, Tanishk",
    editor = {Hamilton, Sil  and
      {\"O}hman, Emily  and
      Hicke, Rebecca M. M.  and
      Bizzoni, Yuri  and
      Bax, Axel  and
      Matthews, Jacob A.  and
      H{\"a}m{\"a}l{\"a}inen, Mika},
    booktitle = "Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities",
    month = jul,
    year = "2026",
    address = "San Diego, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.nlp4dh-1.28/",
    pages = "314--319",
    ISBN = "979-8-89176-427-9",
    abstract = "This paper introduces a computational frameworkfor evaluating structural properties ofthe undeciphered Indus script.The study usesa corpus of 6,579 inscriptions.The analyticalapproach combines unsupervised visual clusteringof sign morphology, entropy-based sequenceanalysis, Kullback-Leibler divergencecomparison, and neural sequence modeling(BiLSTM). The results indicate directionalasymmetry and structured combinatorial patternsin sign sequences. We conclude that theIndus sign sequences exhibit statistical propertiesconsistent with structured symbolic systemsand not easily explained by random generation."
}