References
- Benites-Lazaro, L. L., Giatti, L., & Giarolla, A. (2018). Topic modeling method for analyzing social actor discourses on climate change, energy and food security. Energy Research & Social Science, 45, 318–330. https://doi.org/10.1016/j.erss.2018.07.031
- Berrang-Ford, L., Siders, A. R., Lesnikowski, A., Fischer, A. P., Callaghan, M. W., Haddaway, N. R., Mach, K. J., Araos, M., Shah, M. A. R., & Wannewitz, M. (2021). A systematic global stocktake of evidence on human adaptation to climate change. Nature Climate Change, 11(11), 989–1000. https://doi.org/10.1038/s41558-021-01170-y
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan.), 993–1022.
- Cachay, S. R., Boecking, B., & Dubrawski1, A. (2021). End-to-end weak supervision. Advances in Neural Information Processing Systems, 34, 1845–1857.
- Callaghan, M. W., Minx, J. C., & Forster, P. M. (2020). A topography of climate change research. Nature Climate Change, 10(2), 118–123. https://doi.org/10.1038/s41558-019-0684-5
- Callaghan, M., Schleussner, C.-F., Nath, S., Lejeune, Q., Knutson, T. R., Reichstein, M., Hansen, G., Theokritoff, E., Andrijevic, M., & Brecha, R. J. (2021). Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies. Nature Climate Change, 11(11), 966–972. https://doi.org/10.1038/s41558-021-01168-6
- Chang, I.-C., Yu, T.-K., Chang, Y.-J., & Yu, T.-Y. (2021). Applying text mining, clustering analysis, and latent dirichlet allocation techniques for topic classification of environmental education journals. Sustainability, 13(19), 10856.
- Chen, X., Zou, L., & Zhao, B. (2019). Detecting climate change deniers on twitter using a deep neural network. Proceedings of the 2019 11th International Conference on Machine Learning and Computing, Zhuhai China (pp. 204–210).
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
- Engineering National Academies of Sciences, Medicine. (2016). Attribution of extreme weather events in the context of climate change. National Academies Press.
- Gao, M., Zhang, Z., Yu, G., Arık, S. O., Davis, L. S., & Pfister, T. (2020). Consistency-based semi-supervised¨ active learning: Towards minimizing labeling cost. European Conference on Computer Vision, Glasgow, UK (pp. 510–526). Springer.
- Goudjil, M., Koudil, M., Bedda, M., & Ghoggali, N. (2018). A novel active learning method using SVM for text classification. International Journal of Automation & Computing, 15(3), 290–298. https://doi.org/10.1007/s11633-015-0912-z
- Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
- Hsu, A., & Rauber, R. (2021). Diverse climate actors show limited coordination in a large-scale text analysis of strategy documents. Communications Earth & Environment, 2(1), 1–12. https://doi.org/10.1038/s43247-021-00098-7
- Intergovernmental Panel on Climate Change. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
- IPCC. (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.
- Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized Bert pretraining approach. arXiv preprint arXiv:1907.11692.
- Lo, K., Lu Wang, L., Neumann, M., Kinney, R., & Weld, D. (2020). S2ORC: The semantic scholar open research corpus. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Seattle, Washington, USA (pp. 4969–4983). Online, 2020. Association for Computational Linguistics.
- Luccioni, A., Baylor, E., & Duchene, N. (2020). Analyzing sustainability reports using natural language processing. arXiv preprint arXiv:2011.08073.
- Marsi, E., Øzturk, P., Aamot, E., Valerjevich Sizov, G., & Van Ardelan, M. (2014). Towards text mining in climate science: Extraction of quantitative variables and their relations. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), 16–23.
- McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. https://doi.org/10.21105/joss.00205
- McInnes, L., Healy, J., Melville, J., & Großberger, L. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. Journal of Open Source Software, arXiv preprint arXiv:1802.03426, 3(29), 861. https://doi.org/10.21105/joss.00861
- National Critical Functions. (2022, October 19). https://www.cisa.gov/national-critical-functions-set
- Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., & Re, C. (2017). Snorkel: Rapid training´ data creation with weak supervision. Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases, Rio de Janeiro, Brazil (Vol. 11, p. 269). NIH Public Access.
- Ratner, A., & Ehrenberg, H. (2022). Snorkel. https://github.com/snorkel-team/snorkel
- Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier chains for multi-label classification. Machine Learning, 85(3), 333–359. https://doi.org/10.1007/s10994-011-5256-5
- Reidmiller, D. R., Avery, C. W., Easterling, D. R., Kunkel, K. E., Lewis, K. L. M., Maycock, T. K., & Stewart, B. C. (2019). Fourth national climate assessment.
- Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China (pp. 3982–3992).
- Reimers, N., & Gurevych, I. (2020). Making monolingual sentence embeddings multilingual using knowledge distillation. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Virtual (Vol. 11).
- Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Slavin Ross, A., Milojevic-Dupont, N., Jaques, N., & Waldman-Brown, A. (2022). Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55(2), 1–96. https://doi.org/10.1145/3485128
- Stephen, H. B., He, B., Ratner, A., & Re, C. (2017). Learning the structure of generative models without´ labeled data. International Conference on Machine Learning, Sydney (pp. 273–282). PMLR.
- Task Committee on Future Weather, Climate Extremes, Mari R Tye, and Jason P Giovannettone. (2021). Impacts of future weather and climate extremes on United States infrastructure: Assessing and prioritizing adaptation actions.
- U.S. Department of Homeland Security. (2020). National Critical Functions: Status update to the critical infrastructure community.
- USGCRP Indicators Calalog. https://www.globalchange.gov/browse/indicators/catalog.
- Varini, F. S., Boyd-Graber, J., Ciaramita, M., & Leippold, M. (2020). ClimaText: A dataset for climate change topic detection. arXiv preprint arXiv:2012.00483.
- Webersinke, N., Kraus, M., Anna Bingler, J., & Leippold, M. (2021). Climatebert: A pretrained language model for climate-related text. arXiv preprint arXiv:2110.12010.