比较3种工具识别德语新闻地名的差异，影响气候事件地理定位结果。
Geolocating News about Extreme Climate Events: A Comparative Analysis of Off-the-Shelf Tools for Toponym Identification in German
- 用Flair、Spacy、Stanza三款工具识别德语文本地名
- 不同工具输出差异显著，导致事件国家定位结果不一
- 研究结果影响媒体对各国气候事件关注度的判断
确定文本中极端气候事件和灾害的地理位置是气候影响与适应研究中的常见问题。通常使用命名实体识别（NER）工具提取地名候选位置。本研究对比分析了三种现成NER工具——Flair、Spacy和Stanza在德语文本中的表现，描述并量化其输出差异，并基于三种方法外在评估它们对事件所在国家判定的准确性。结果显示，工具间差异会传递至下游任务，导致文档地理焦点判断不同，进而影响对德国媒体中各国气候事件突出程度的结论。
原文摘要 · Abstract (English)
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 study, we conduct a comparative analysis of three off-the-shelf NER tools, namely Flair, Spacy and Stanza. We describe and quantify differences between their outputs for German news articles and evaluate them extrinsically based on three methods to determine the country where events took place. We show how their contrasts are propagated into downstream tasks and can yield distinct decisions about a document's geographical focus, which, in turn, can impact conclusions about countries' prominence in German media.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。