Geolocating News about Extreme Climate Events: A Comparative Analysis of Off-the-Shelf Tools for Toponym Identification in German
For climate impact researchers using NER tools to geolocate events, this study highlights that tool selection can bias results, but the findings are incremental as they confirm known variability without proposing a new solution.
The study compares three off-the-shelf NER tools (Flair, Spacy, Stanza) for identifying toponyms in German news articles about extreme climate events, finding that tool choice significantly affects downstream geolocation decisions and conclusions about country prominence in media coverage.
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.