A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events
This work provides a resource for researchers and practitioners in areas like travel planning and supply chain management, but it is incremental as it focuses on dataset creation rather than novel methods.
The authors tackled the challenge of extracting fine-grained mobility and industry events from heterogeneous German text streams by creating a corpus annotated with detailed geo-entities and 15 traffic- and industry-related relations, enabling training and evaluation of named entity recognition and relation extraction systems.
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.