Natural Language Processing Methods Application in Defense Budget Analysis-Reference-Cited by-同舟云学术

Natural Language Processing Methods Application in Defense Budget Analysis

Published:2024-08-01 Issue:2 Volume:19 Page:290-307
ISSN:2344-5416
Container-title:Studies in Business and Economics
language:en
Short-container-title:

Author:

Zatonatska Tetiana¹,Kharlamova Ganna²,Pakholchuk Vadym¹,Syzov Alim¹

Affiliation:

1. Taras Shevchenko National University of Kyiv , Ukraine

2. Taras Shevchenko National University of Kyiv , Ukraine ; Lucian Blaga University of Sibiu , Romania

Abstract

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.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/sbe-2024-0039

Reference23 articles.

1. Bochkay, K. (2014) Enhancing empirical accounting models with textual information (Doctoral dissertation). Rutgers University-Graduate School-Newark.

2. Bruno, N., Jun, T., Tessier, H. (2019) Natural language processing and classification methods for the maintenance and optimization of US weapon systems. In systems and information engineering design symposium, SIEDS, https://doi.org/10.1109/sieds.2019.8735587

3. Chakraborty, B. and Bhattacharjee, T. (2020) A review on textual analysis of corporate disclosure according to the evolution of different automated methods.Journal of Financial Reporting and Accounting, 18(4):757-777.

4. Damasevicius, R. (2023) Artificial intelligence techniques in economic analysis, Economic Analysis Letters, 2(2): 52–59. https://doi.org/10.58567/eal02020007

5. Davies, J., Arana-Catania, M., Procter, R., van Lier, F.A. and He, Y. (2021) Evaluating the application of NLP tools in mainstream participatory budgeting processes in Scotland, In Proceedings of the 14th International Conference on Theory and Practice of Electronic Governance, pp. 362-366.