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Word frequency–rank relationship in tagged texts

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  • Chacoma, Andrés
  • Zanette, Damián H.

Abstract

We analyze the frequency–rank relationship in sub-vocabularies corresponding to three different grammatical classes (nouns, verbs, and others) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role. Comparing with a null hypothesis which assumes that words belonging to each class are uniformly distributed across the frequency–ranked vocabulary of the whole work, we disclose statistically significant differences between the three classes. This results point to the fact that frequency–rank relationships may reflect linguistic features associated with grammatical function.

Suggested Citation

  • Chacoma, Andrés & Zanette, Damián H., 2021. "Word frequency–rank relationship in tagged texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    • Handle: RePEc:eee:phsmap:v:574:y:2021:i:c:s0378437121002922
    DOI: 10.1016/j.physa.2021.126020
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    References listed on IDEAS

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    1. Ficcadenti, Valerio & Cerqueti, Roy & Ausloos, Marcel & Dhesi, Gurjeet, 2020. "Words ranking and Hirsch index for identifying the core of the hapaxes in political texts," Journal of Informetrics, Elsevier, vol. 14(3).
    2. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
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    1. Valero, Jordi & Pérez-Casany, Marta & Duarte-López, Ariel, 2022. "The Zipf-Polylog distribution: Modeling human interactions through social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).

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