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

arXiv: https://arxiv.org/abs/2004.03283 · HTML: https://mlsanity.com/paper/2004.03283/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data](https://mlsanity.com/paper/2403.12749/) (2024)
- [MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain](https://mlsanity.com/paper/2108.06955/) (2021)
- [Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction](https://mlsanity.com/paper/2111.09035/) (2021)
- [ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts](https://mlsanity.com/paper/2112.06724/) (2021)
- [A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2204.04980/) (2022)
- [Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia](https://mlsanity.com/paper/2311.13755/) (2023)
- [CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction](https://mlsanity.com/paper/2403.15322/) (2024)

## Abstract (arXiv)

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.