MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain

TL;DR

MobIE is a comprehensive German dataset for named entity recognition, entity linking, and relation extraction in the mobility domain, enabling joint learning for these tasks.

Contribution

First German dataset combining annotations for NER, EL, and RE, with human and weakly-supervised annotations for mobility-related texts.

Findings

Contains 20.5K annotated entities with linking to a knowledge base

Includes a subset with human-annotated n-ary relations

Supports joint and multi-task learning approaches

Abstract

We present MobIE, a German-language dataset, which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. To the best of our knowledge, this is the first German-language dataset that combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks. We make MobIE public at https://github.com/dfki-nlp/mobie.