[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/pie/dsedps/2024-317.html
   My bibliography  Save this paper

Natural Language Processing Techniques for Long Financial Document

Author

Listed:
  • Maria Saveria Mavillonio
Abstract
In finance, Natural Language Processing (NLP) has become both a powerful and challenging tool, as extensive unstructured documents—such as business plans, financial reports, and regulatory filings—hold essential insights for strategic decision-making. This paper reviews the progression of NLP text representation methods, from foundational models to advanced Transformer architectures that greatly enhance semantic and contextual analysis. Yet, these models encounter limitations when applied to long financial documents, where computational efficiency and contextual coherence are critical. Recent innovations, including sparse attention mechanisms and domain-specific model adaptations, have improved the processing of lengthy texts, allowing for more accurate analysis of financial documents by capturing field-specific semantics. This paper also highlights the transformative role of NLP in financial analysis, especially where structured data is limited. Selecting the most suitable model for specific tasks is essential for maximizing NLP's impact in finance. Organized to provide a thorough overview, the paper covers text representation techniques, strategies for handling long texts, and applications in finance, establishing a foundation for advancing NLP-driven data analysis in this field.

Suggested Citation

  • Maria Saveria Mavillonio, 2024. "Natural Language Processing Techniques for Long Financial Document," Discussion Papers 2024/317, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
  • Handle: RePEc:pie:dsedps:2024/317
    Note: ISSN 2039-1854
    as

    Download full text from publisher

    File URL: https://www.ec.unipi.it/documents/Ricerca/papers/2024-317.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Long Text; Financial Document Representation; Natural Language Processing; Transformers;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G2 - Financial Economics - - Financial Institutions and Services
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

    Statistics

    Access and download statistics

    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:pie:dsedps:2024/317. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/dspisit.html .

    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.