NATURAL LANGUAGE PROCESSING METHODS APPLICATION IN DEFENSE BUDGET ANALYSIS

Tetiana Zatonatska, Ganna Kharlamova, Vadym Pakholchuk and Alim Syzov
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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

Studies in Business and Economics, 2024, vol. 19, issue 2, 290-307

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.

Keywords: Natural Language Processing; word embedding; defense budget; defense expenditures; transparency (search for similar items in EconPapers)
Date: 2024
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