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Monitoring mobility- and industry-relevant events is important in areas suchas personal travel planning and supply chain management, but extracting eventspertaining to specific companies, transit routes and locations fromheterogeneous, high-volume text streams remains a significant challenge. Thiswork describes a corpus of German-language documents which has been annotatedwith fine-grained geo-entities, such as streets, stops and routes, as well asstandard named entity types. It has also been annotated with a set of 15traffic- and industry-related n-ary relations and events, such as accidents,traffic jams, acquisitions, and strikes. The corpus consists of newswire texts,Twitter messages, and traffic reports from radio stations, police and railwaycompanies. It allows for training and evaluating both named entity recognitionalgorithms that aim for fine-grained typing of geo-entities, as well as n-aryrelation extraction systems.

A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events