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Natural Language Processing Methods Application In Defense Budget Analysis

Author

Listed:
  • Tetiana ZATONATSKA

    (Taras Shevchenko National University of Kyiv, Ukraine)

  • Ganna KHARLAMOVA

    (Taras Shevchenko National University of Kyiv, Ukraine & Lucian Blaga University of Sibiu, Romania)

  • Vadym PAKHOLCHUK

    (Taras Shevchenko National University of Kyiv, Ukraine)

  • Alim SYZOV

    (Taras Shevchenko National University of Kyiv, Ukraine)

Abstract

Transferring to economy 5.0 makes a great emphasis on Artificial Intelligence technologies implementation in civil and military areas. The aim of the article is to model relation between the Ukrainian Ministry of Defense budget programs and strategic goals and tasks. The classical budget analysis methodology is extended with NLP technics. The analysis is performed for defense budget program 2101020 - Ensuring the activities of the Armed Forces of Ukraine, training of personnel and troops, medical support of personnel, military service veterans and their family members, and war veterans. Either TF-IDF or more advanced NLP methods are used along with Python libraries and packages. It is found that some goals intersect with each other by semantic similarity. Despite the lack of data, we build proof of concept machine learning model and proved its effectiveness.

Suggested Citation

  • Tetiana ZATONATSKA & Ganna KHARLAMOVA & Vadym PAKHOLCHUK & Alim SYZOV, 2024. "Natural Language Processing Methods Application In Defense Budget Analysis," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 19(2), pages 290-307, August.
    • Handle: RePEc:blg:journl:v:19:y:2024:i:2:p:290-307
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    References listed on IDEAS

    as
    1. Maciej Wujec, 2021. "Analysis of the Financial Information Contained in the Texts of Current Reports: A Deep Learning Approach," JRFM, MDPI, vol. 14(12), pages 1-17, December.
    2. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    3. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    4. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    Full references (including those not matched with items on IDEAS)

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