  Monitoring mobility- and industry-relevant events is important in areas such
as personal travel planning and supply chain management, but extracting events
pertaining to specific companies, transit routes and locations from
heterogeneous, high-volume text streams remains a significant challenge. This
work describes a corpus of German-language documents which has been annotated
with fine-grained geo-entities, such as streets, stops and routes, as well as
standard named entity types. It has also been annotated with a set of 15
traffic- 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 railway
companies. It allows for training and evaluating both named entity recognition
algorithms that aim for fine-grained typing of geo-entities, as well as n-ary
relation extraction systems.
