Named Entity Recognition for Climate Change Research
Abstract
Abstract The rapid growth of scientific literature on anthropogenic climate change necessitates automated information extraction to manage and synthesise complex, multi-scalar climate phenomena. Although the research community increasingly relies on Natural Language Processing (NLP), particularly Named Entity Recognition (NER), existing resources in this domain lack broad thematic coverage and rigorous multi-expert validation. To address this gap, we introduce CliReNER, a novel NER resource for climate research literature comprising 28 fine-grained entity types. The resource includes two datasets: CliReNERsilver, an author-annotated dataset, and CliReNERgold, a high-fidelity benchmark annotated by 12 experts from various scientific background. We systematically compare CliReNER with existing climate NLP datasets and conduct a comprehensive benchmark across 23 deep neural models, covering fine-tuned encoder models, generalist NER architectures, and zero-shot commercial Large Language Models (LLMs). Our results show that fine-tuned generalist NER architectures achieve the strongest overall performance (strict Micro F1 = 0.62). For traditional encoder-based models, domain-adapted variants offer a clear advantage. In contrast, although zero-shot LLMs are robust to high lexical diversity, they consistently struggle with strict boundary detection, which significantly reduces their overall efficacy. We release our datasets, guidelines, and fine-tuned models to support advanced…
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