两篇新研究论文探讨了用于分析德国媒体对极端气候事件新闻报道的自然语言处理技术。一篇论文比较了 Flair、Spacy 和 Stanza 等现成命名实体识别 (NER) 工具在识别地名和对事件进行地理定位方面的性能。第二篇论文研究了使用主题模型作为二元分类器来改进相关新闻文章的检索，并将这种方法与微调的文本嵌入分类器和开源大语言模型进行了比较。 AI
影响 这些方法可以提高气候影响研究中媒体报道分析的准确性和效率。
排序理由 两篇 arXiv 论文提出了用于分析气候事件新闻的新型自然语言处理方法。
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arXiv:2605.03414v1 Announce Type: new Abstract: Determining the geolocation of extreme climate events and disasters in texts is a common problem in climate impact and adaptation research. Named-entity recognition (NER) tools are typically used to identify a pool of toponyms that …
arXiv:2605.03450v1 Announce Type: new Abstract: In studies of media coverage of extreme climate events, NLP methods have become indispensable for identifying relevant texts in large news databases. Still, enough annotated data to train accurate deep learning-based classifiers fro…
In studies of media coverage of extreme climate events, NLP methods have become indispensable for identifying relevant texts in large news databases. Still, enough annotated data to train accurate deep learning-based classifiers from scratch is often not available. Topic Models h…
Determining the geolocation of extreme climate events and disasters in texts is a common problem in climate impact and adaptation research. Named-entity recognition (NER) tools are typically used to identify a pool of toponyms that serve as candidate event locations. In this stud…