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, <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1109/sieds.2019.8735587" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1109/sieds.2019.8735587</a>">https://doi.org/10.1109/sieds.2019.8735587</ext-link>
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.
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.
Devlin, J., Chang, M. W., Lee, K. and Toutanova, K. (2018) Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805.
El-Haj, M. et al. (2019) In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse.Journal of Business Finance & Accounting, 46(3-4): 265-306.
Fernandez-Cortez, V. et al. (2020) Can artificial intelligence help optimize the public budgeting process? Lessons about smartness and public value from the Mexican federal government. In 2020 Seventh International Conference on eDemocracy & eGovernment, ICEDEG, pp.312-315.
Fisher, I. E., Garnsey, M. R. and M. E. Hughes, M. E. (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, 23(3): 157-214.
Hájek, P. (2018) Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns.Neural Computing and Applications, 29: 343-358.
Henry, E. and Leone, A.J.. (2016) Measuring qualitative information in capital markets research: Comparison of alternative methodologies to measure disclosure tone. The Accounting Review, 91(1): 153-178.
Le, Q., Mikolov, T. (2014) Distributed representations of sentences and documents. In International conference on machine learning, PMLR, pp. 1188-1196.
Loughran, T. and McDonald, B. (2011) When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of finance, 66(1): 35-65.
Luo, Y.and Zhou, L. (2020)Textual tone in corporate financial disclosures: a survey of the literature.International Journal of Disclosure and Governance, 17(2-3): 101-110.
Siano, F. and Wysocki, P. (2021) Transfer learning and textual analysis of accounting disclosures: applying big data methods to small (ER) datasets. Accounting Horizons, 35(3): 217-244.
Valle-Cruz, D., Fernandez-Cortez, V. and Gil-Garcia, J. R. (2022) From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation, Government Information Quarterly, 39(2): 101-144.
Wujec, M. (2021) Analysis of the Financial Information Contained in the Texts of Current Reports: A Deep Learning Approach.Journal of Risk and Financial Management, 14(12): 582.
Zhang, M.C. et al. (2019) Text data sources in archival accounting research: Insights and strategies for accounting systems’ scholars.Journal of Information Systems, 33(1):145-180.
Zuiderwijk, A. et al. (2021) Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda.Government Information Quarterly, 38(3):101577.