IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v574y2021ics0378437121002922.html
   My bibliography  Save this article

Word frequency–rank relationship in tagged texts

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121002922
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126020?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "State Space Approach to Adaptive Artificial Intelligence Modeling: Application to Financial Portfolio with Fuzzy System," CARF F-Series CARF-F-422, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. Wilson, Christopher & van der Velden, Maja, 2022. "Sustainable AI: An integrated model to guide public sector decision-making," Technology in Society, Elsevier, vol. 68(C).
    3. Yi, Jing & Canning, Patrick N. & Ge, Houtian & Rehkamp, Sarah & Gomez, Miguel I., 2023. "A national database of highly perishable fresh produce production with temporal and spatial resolution," 2023 Annual Meeting, July 23-25, Washington D.C. 335590, Agricultural and Applied Economics Association.
    4. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    5. Kim, Sung Wook & Oh, Ki-Yong & Lee, Seungchul, 2022. "Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries," Applied Energy, Elsevier, vol. 315(C).
    6. Xuequn Wang & Xiaolin Lin & Bin Shao, 2023. "Artificial intelligence changes the way we work: A close look at innovating with chatbots," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(3), pages 339-353, March.
    7. Himanshu Sharma & Anu G. Aggarwal, 2022. "Segmenting Reviewers Based on Reviewer and Review Characteristics," International Journal of Business Analytics (IJBAN), IGI Global, vol. 9(1), pages 1-20, January.
    8. Fekadu Agmas Wassie & László Péter Lakatos, 2024. "Artificial intelligence and the future of the internal audit function," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    9. Joaquim AP Braga & António R Andrade, 2019. "Optimizing maintenance decisions in railway wheelsets: A Markov decision process approach," Journal of Risk and Reliability, , vol. 233(2), pages 285-300, April.
    10. Valerio Ficcadenti & Roy Cerqueti & Ciro Hosseini Varde’i, 2023. "A rank-size approach to analyse soccer competitions and teams: the case of the Italian football league “Serie A"," Annals of Operations Research, Springer, vol. 325(1), pages 85-113, June.
    11. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    12. Changhao Zhang & Mengyu Ren, 2023. "Customer service robot model based on e-commerce dual-channel channel supply coordination and compensation strategy in the perspective of big data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(2), pages 591-601, April.
    13. Chen, Xia & Geyer, Philipp, 2022. "Machine assistance in energy-efficient building design: A predictive framework toward dynamic interaction with human decision-making under uncertainty," Applied Energy, Elsevier, vol. 307(C).
    14. Moll, Jodie & Yigitbasioglu, Ogan, 2019. "The role of internet-related technologies in shaping the work of accountants: New directions for accounting research," The British Accounting Review, Elsevier, vol. 51(6).
    15. Martin Obschonka & David B. Audretsch, 2020. "Artificial intelligence and big data in entrepreneurship: a new era has begun," Small Business Economics, Springer, vol. 55(3), pages 529-539, October.
    16. Efrat Taig & Ohad Ben-Shahar, 2019. "Gradient Surfing: A New Deterministic Approach for Low-Dimensional Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 855-878, March.
    17. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    18. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "State Space Approach to Adaptive Fuzzy Modeling: Application to Financial Investment," CIRJE F-Series CIRJE-F-1067, CIRJE, Faculty of Economics, University of Tokyo.
    19. Gianluca Gabrielli & Alice Medioli & Paolo Andrei, 2022. "Accounting and Big Data: Trends, opportunities and direction for practitioners and researchers," FINANCIAL REPORTING, FrancoAngeli Editore, vol. 2022(2), pages 89-112.
    20. Daniel Beverungen & Joos C. A. M. Buijs & Jörg Becker & Claudio Ciccio & Wil M. P. Aalst & Christian Bartelheimer & Jan Brocke & Marco Comuzzi & Karsten Kraume & Henrik Leopold & Martin Matzner & Jan , 2021. "Seven Paradoxes of Business Process Management in a Hyper-Connected World," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(2), pages 145-156, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:574:y:2021:i:c:s0378437121002922. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.