---
res:
  bibo_abstract:
  - 'Recent theoretical advancement of information density in natural language has
    brought the following question on desk: To what degree does natural language exhibit
    periodicity pattern in its encoded information? We address this question by introducing
    a new method called AutoPeriod of Surprisal (APS). APS adopts a canonical periodicity
    detection algorithm and is able to identify any significant periods that exist
    in the surprisal sequence of a single document. By applying the algorithm to a
    set of corpora, we have obtained the following interesting results: Firstly, a
    considerable proportion of human language demonstrates a strong pattern of periodicity
    in information; Secondly, new periods that are outside the distributions of typical
    structural units in text (e.g., sentence boundaries, elementary discourse units,
    etc.) are found and further confirmed via harmonic regression modeling. We conclude
    that the periodicity of information in language is a joint outcome from both structured
    factors and other driving factors that take effect at longer distances. The advantages
    of our periodicity detection method and its potentials in LLM-generation detection
    are further discussed.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Yulin
      foaf_name: Ou, Yulin
      foaf_surname: Ou
  - foaf_Person:
      foaf_givenName: Yu
      foaf_name: Wang, Yu
      foaf_surname: Wang
  - foaf_Person:
      foaf_givenName: Yang
      foaf_name: Xu, Yang
      foaf_surname: Xu
  - foaf_Person:
      foaf_givenName: Hendrik
      foaf_name: Buschmeier, Hendrik
      foaf_surname: Buschmeier
      foaf_workInfoHomepage: http://www.librecat.org/personId=76456
    orcid: 0000-0002-9613-5713
  dct_date: 2026^xs_gYear
  dct_language: eng
  dct_title: Identifying the periodicity of information in natural language@
...