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An industry Bachelor or Master thesis can explore a deep technical question your product team has no time to investigate—if the research question, data, and supervision are structured properly from day one.
A GreenDIA and Klimatkollen technical report separates extraction accuracy from source-report quality and proposes 30 errors and issues that human annotators and Climate NLP systems can assess.
NLP4Climate is a growing international research community connecting people who use natural language processing to understand, analyse, and act on climate change. Monthly calls, self-organised focus groups, open talks, and a Slack community.
A groundbreaking reproducibility study on 29 climate-related NLP datasets reveals that most tasks rely on surface-level keyword patterns rather than deep understanding, with 96% of datasets containing annotation issues that compromise evaluation reliability.
