2021/08/16 by Leonhard Hennig, Hennig, Leonhard, Phuc Tran Truong +3
Computer Science · Decision Sciences · #Computation and Language (cs.CL) #Data Quality and Management #FOS: Computer and information sciences #Natural Language Processing Techniques #Topic Modeling #Web Data Mining and Analysis #cs.CL
paper · pdf · doi:10.48550/arxiv.2108.06955
Accepted at KONVENS 2021. 5 pages, 3 figures, 5 tables
openalex publication_date 2021/08/16 · arxiv created 2022/03/28 · arxiv updated 2022/03/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.