Datasets:
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- de
licenses:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: mobie
pretty_name: MobIE
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- structure-prediction
task_ids:
- named-entity-recognition
Dataset Card for "MobIE"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/dfki-nlp/mobie
- Repository: https://github.com/dfki-nlp/mobie
- Paper: https://aclanthology.org/2021.konvens-1.22/
- Point of Contact: See https://github.com/dfki-nlp/mobie
- Size of downloaded dataset files: 7.8 MB
- Size of the generated dataset: 1.9 MB
- Total amount of disk used: 9.7 MB
Dataset Summary
This script is for loading the MobIE dataset from https://github.com/dfki-nlp/mobie.
MobIE is 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. The dataset combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks.
This version of the dataset loader provides NER tags only. NER tags use the BIO tagging scheme.
For more details see https://github.com/dfki-nlp/mobie and https://aclanthology.org/2021.konvens-1.22/.
Supported Tasks and Leaderboards
- Tasks: Named Entity Recognition
- Leaderboards:
Languages
German
Dataset Structure
Data Instances
- Size of downloaded dataset files: 7.8 MB
- Size of the generated dataset: 1.9 MB
- Total amount of disk used: 9.7 MB
An example of 'train' looks as follows.
{
'id': 'http://www.ndr.de/nachrichten/verkehr/index.html#2@2016-05-04T21:02:14.000+02:00',
'tokens': ['Vorsicht', 'bitte', 'auf', 'der', 'A28', 'Leer', 'Richtung', 'Oldenburg', 'zwischen', 'Zwischenahner', 'Meer', 'und', 'Neuenkruge', 'liegen', 'Gegenstände', '!'],
'ner_tags': [0, 0, 0, 0, 19, 13, 0, 13, 0, 11, 12, 0, 11, 0, 0, 0]
}
Data Fields
The data fields are the same among all splits.
id: astringfeature.tokens: alistofstringfeatures.ner_tags: alistof classification labels, with possible values includingO(0),B-date(1),I-date(2),B-disaster-type(3),I-disaster-type(4), ...
Data Splits
| Train | Dev | Test | |
|---|---|---|---|
| MobIE | 4785 | 1082 | 1210 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{hennig-etal-2021-mobie,
title = "{M}ob{IE}: A {G}erman Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain",
author = "Hennig, Leonhard and
Truong, Phuc Tran and
Gabryszak, Aleksandra",
booktitle = "Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)",
month = "6--9 " # sep,
year = "2021",
address = {D{\"u}sseldorf, Germany},
publisher = "KONVENS 2021 Organizers",
url = "https://aclanthology.org/2021.konvens-1.22",
pages = "223--227",
}