AI-Enabled Policy Content Modeling: A Systems Approach
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62554-1_25
Reference13 articles.
1. Bhada, S. V., & Krishnan, R. (2021). A model centric framework and approach for complex systems policy. IEEE Systems Journal, 15(1), 215–225. https://doi.org/10.1109/JSYST.2020.3003204
2. Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297. https://doi.org/10.1093/PAN/MPS028
3. Hagen, L., Uzuner, O., Kotfila, C., Harrison, T. M., & LaManna, D. (2015). Understanding citizens’ direct policy suggestions to the federal government: A natural language processing and topic modeling approach. In Proceedings of the Annual Hawaii International Conference on System Sciences, 2015 March (pp. 2134–2143). https://doi.org/10.1109/HICSS.2015.257
4. Chami, M., Abdoun, N., & Bruel, J. (2022). Artificial intelligence capabilities for effective model-based systems engineering: A vision paper. INCOSE International Symposium, 32(1), 1160–1174. https://doi.org/10.1002/iis2.12988
5. Riesener, M., Dölle, C., Becker, A., Gorbatcheva, S., Rebentisch, E., & Schuh, G. (2021). Application of natural language processing for systematic requirement management in model-based systems engineering. INCOSE International Symposium, 31(1), 806–815. https://doi.org/10.1002/J.2334-5837.2021.00871.X
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