Datasets:
File size: 28,635 Bytes
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---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
task_ids:
- named-entity-recognition
paperswithcode_id: mobie
pretty_name: MobIE
tags:
- structure-prediction
dataset_info:
- config_name: ee
features:
- name: id
dtype: string
- name: text
dtype: string
- name: entity_mentions
list:
- name: id
dtype: string
- name: text
dtype: string
- name: start
dtype: int32
- name: end
dtype: int32
- name: type
dtype:
class_label:
names:
'0': date
'1': disaster-type
'2': distance
'3': duration
'4': event-cause
'5': location
'6': location-city
'7': location-route
'8': location-stop
'9': location-street
'10': money
'11': number
'12': organization
'13': organization-company
'14': org-position
'15': percent
'16': person
'17': set
'18': time
'19': trigger
- name: refids
list:
- name: key
dtype: string
- name: value
dtype: string
- name: event_mentions
list:
- name: id
dtype: string
- name: trigger
struct:
- name: id
dtype: string
- name: text
dtype: string
- name: start
dtype: int32
- name: end
dtype: int32
- name: arguments
list:
- name: id
dtype: string
- name: text
dtype: string
- name: start
dtype: int32
- name: end
dtype: int32
- name: role
dtype:
class_label:
names:
'0': no_arg
'1': location
'2': delay
'3': direction
'4': start_loc
'5': end_loc
'6': start_date
'7': end_date
'8': cause
'9': jam_length
'10': route
- name: type
dtype:
class_label:
names:
'0': date
'1': disaster-type
'2': distance
'3': duration
'4': event-cause
'5': location
'6': location-city
'7': location-route
'8': location-stop
'9': location-street
'10': money
'11': number
'12': organization
'13': organization-company
'14': org-position
'15': percent
'16': person
'17': set
'18': time
'19': trigger
- name: event_type
dtype:
class_label:
names:
'0': O
'1': Accident
'2': CanceledRoute
'3': CanceledStop
'4': Delay
'5': Obstruction
'6': RailReplacementService
'7': TrafficJam
- name: tokens
sequence: string
- name: pos_tags
sequence: string
- name: lemma
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-date
'2': B-disaster-type
'3': B-distance
'4': B-duration
'5': B-event-cause
'6': B-location
'7': B-location-city
'8': B-location-route
'9': B-location-stop
'10': B-location-street
'11': B-money
'12': B-number
'13': B-organization
'14': B-organization-company
'15': B-org-position
'16': B-percent
'17': B-person
'18': B-set
'19': B-time
'20': B-trigger
'21': I-date
'22': I-disaster-type
'23': I-distance
'24': I-duration
'25': I-event-cause
'26': I-location
'27': I-location-city
'28': I-location-route
'29': I-location-stop
'30': I-location-street
'31': I-money
'32': I-number
'33': I-organization
'34': I-organization-company
'35': I-org-position
'36': I-percent
'37': I-person
'38': I-set
'39': I-time
'40': I-trigger
splits:
- name: train
num_bytes: 1869427
num_examples: 788
- name: test
num_bytes: 1117030
num_examples: 484
- name: validation
num_bytes: 365928
num_examples: 152
download_size: 8190212
dataset_size: 3352385
- config_name: el
features:
- name: id
dtype: string
- name: text
dtype: string
- name: entity_mentions
list:
- name: id
dtype: string
- name: text
dtype: string
- name: start
dtype: int32
- name: end
dtype: int32
- name: type
dtype:
class_label:
names:
'0': date
'1': disaster-type
'2': distance
'3': duration
'4': event-cause
'5': location
'6': location-city
'7': location-route
'8': location-stop
'9': location-street
'10': money
'11': number
'12': organization
'13': organization-company
'14': org-position
'15': percent
'16': person
'17': set
'18': time
'19': trigger
- name: refids
list:
- name: key
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 1345663
num_examples: 2115
- name: test
num_bytes: 503058
num_examples: 623
- name: validation
num_bytes: 298974
num_examples: 494
download_size: 8190212
dataset_size: 2147695
- config_name: ner
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-date
'2': B-disaster-type
'3': B-distance
'4': B-duration
'5': B-event-cause
'6': B-location
'7': B-location-city
'8': B-location-route
'9': B-location-stop
'10': B-location-street
'11': B-money
'12': B-number
'13': B-organization
'14': B-organization-company
'15': B-org-position
'16': B-percent
'17': B-person
'18': B-set
'19': B-time
'20': B-trigger
'21': I-date
'22': I-disaster-type
'23': I-distance
'24': I-duration
'25': I-event-cause
'26': I-location
'27': I-location-city
'28': I-location-route
'29': I-location-stop
'30': I-location-street
'31': I-money
'32': I-number
'33': I-organization
'34': I-organization-company
'35': I-org-position
'36': I-percent
'37': I-person
'38': I-set
'39': I-time
'40': I-trigger
splits:
- name: train
num_bytes: 1112606
num_examples: 2115
- name: test
num_bytes: 354244
num_examples: 623
- name: validation
num_bytes: 251031
num_examples: 494
download_size: 8190212
dataset_size: 1717881
- config_name: re
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: entities
sequence:
list: int32
- name: entity_roles
sequence:
class_label:
names:
'0': no_arg
'1': trigger
'2': location
'3': delay
'4': direction
'5': start_loc
'6': end_loc
'7': start_date
'8': end_date
'9': cause
'10': jam_length
'11': route
- name: entity_types
sequence:
class_label:
names:
'0': date
'1': disaster-type
'2': distance
'3': duration
'4': event-cause
'5': location
'6': location-city
'7': location-route
'8': location-stop
'9': location-street
'10': money
'11': number
'12': organization
'13': organization-company
'14': org-position
'15': percent
'16': person
'17': set
'18': time
'19': trigger
- name: event_type
dtype:
class_label:
names:
'0': O
'1': Accident
'2': CanceledRoute
'3': CanceledStop
'4': Delay
'5': Obstruction
'6': RailReplacementService
'7': TrafficJam
- name: entity_ids
sequence: string
splits:
- name: train
num_bytes: 1048457
num_examples: 1199
- name: test
num_bytes: 501336
num_examples: 609
- name: validation
num_bytes: 179001
num_examples: 228
download_size: 8190212
dataset_size: 1728794
---
# Dataset Card for "MobIE"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
- **Repository:** [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
- **Paper:** [https://aclanthology.org/2021.konvens-1.22/](https://aclanthology.org/2021.konvens-1.22/)
- **Point of Contact:** See [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
- **Size of downloaded dataset files:** 8.2 MB
- **Size of the generated dataset:** 1.7 MB
- **Total amount of disk used:** 9.9 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 configurations for:
- Named Entity Recognition (`ner`): NER tags use the `BIO` tagging scheme
- Entity Linking (`el`): Entity mentions are linked to an internal knowledge base and Open Street Map
- Relation Extraction (`re`): n-ary Relation Extraction
- Event Extraction (`ee`): formatted similar to https://github.com/nlpcl-lab/ace2005-preprocessing?tab=readme-ov-file#format
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, Entity Linking, n-ary Relation Extraction, Event Extraction
- **Leaderboards:**
### Languages
German
## Dataset Structure
### Data Instances
#### ner
- **Size of downloaded dataset files:** 8.2 MB
- **Size of the generated dataset:** 1.7 MB
- **Total amount of disk used:** 10.9 MB
An example of 'train' looks as follows.
```json
{
"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]
}
```
#### el
- **Size of downloaded dataset files:** 8.2 MB
- **Size of the generated dataset:** 2.1 MB
- **Total amount of disk used:** 10.3 MB
An example of 'train' looks as follows.
```json
{
"id": "1108129826844672001",
"text": "#S4 #RegioNDS #Teilausfall #Mellendorf(23.03)> #Bennemühlen(23.07). Grund: technische Störung an der Strecke. Bitte nutzen Sie #RB38 nach Soltau über Bennemühlen Abfahrt: 23:08 Uhr vom Gleis 2",
"entity_mentions": [
{
"text": "#S4",
"start": 0,
"end": 1,
"char_start": 0,
"char_end": 3,
"type": 7,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "24007"
}
]
},
{
"text": "#RegioNDS",
"start": 1,
"end": 2,
"char_start": 4,
"char_end": 13,
"type": 13,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
},
{
"text": "#Teilausfall",
"start": 2,
"end": 3,
"char_start": 14,
"char_end": 26,
"type": 19,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
},
{
"text": "#Mellendorf",
"start": 3,
"end": 4,
"char_start": 27,
"char_end": 38,
"type": 8,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "8003957"
}
]
},
{
"text": "23.03",
"start": 5,
"end": 6,
"char_start": 39,
"char_end": 44,
"type": 0,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
},
{
"text": "#Bennemühlen",
"start": 8,
"end": 9,
"char_start": 47,
"char_end": 59,
"type": 6,
"entity_id": "29589800",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "29589800"
},
{
"key": "osm_id",
"value": "29589800"
}
]
},
{
"text": "23.07",
"start": 10,
"end": 11,
"char_start": 60,
"char_end": 65,
"type": 0,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
},
{
"text": "technische Störung",
"start": 15,
"end": 17,
"char_start": 76,
"char_end": 94,
"type": 4,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
},
{
"text": "#RB38",
"start": 24,
"end": 25,
"char_start": 128,
"char_end": 133,
"type": 7,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "23138"
}
]
},
{
"text": "Soltau",
"start": 26,
"end": 27,
"char_start": 139,
"char_end": 145,
"type": 6,
"entity_id": "1809016",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "-1809016"
},
{
"key": "osm_id",
"value": "1809016"
}
]
},
{
"text": "Bennemühlen",
"start": 28,
"end": 29,
"char_start": 151,
"char_end": 162,
"type": 8,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "8000871"
}
]
},
{
"text": "23:08 Uhr",
"start": 31,
"end": 33,
"char_start": 172,
"char_end": 181,
"type": 18,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
},
{
"text": "2",
"start": 35,
"end": 36,
"char_start": 192,
"char_end": 193,
"type": 11,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
}
]
}
```
#### re
- **Size of downloaded dataset files:** 8.2 MB
- **Size of the generated dataset:** 1.7 MB
- **Total amount of disk used:** 10.9 MB
An example of 'train' looks as follows.
```json
{
"id": "1111185208647274501_1",
"text": "RT @SBahn_Stuttgart: 🚨Störung🚨 Derzeit steht eine #S2 Richtung Filderstadt mit einer Türstörung in Stg-Rohr. Es kommt auf den Linien #S1, #…",
"tokens": ["RT", "@SBahn_Stuttgart", ":", "🚨", "Störung", "🚨 ", "Derzeit", "steht", "eine", "#S2", "Richtung", "Filderstadt", "mit", "einer", "Türstörung", "in", "Stg", "-", "Rohr", ".", "Es", "kommt", "auf", "den", "Linien", "#S1", ",", "#", "…"],
"entities": [[1, 2], [4, 5], [9, 10], [11, 12], [14, 15], [16, 19], [25, 26]],
"entity_roles": [0, 1, 2, 0, 0, 0, 0],
"entity_types": [13, 4, 7, 6, 4, 8, 7],
"event_type": 5,
"entity_ids": ["NIL", "NIL", "NIL", "2796535", "NIL", "NIL", "NIL"]
}
```
#### ee
- **Size of downloaded dataset files:** 8.2 MB
- **Size of the generated dataset:** 3.7 MB
- **Total amount of disk used:** 11.9 MB
An example of 'train' looks as follows.
```json
{
"id": "1111185208647274501",
"text": "RT @SBahn_Stuttgart: 🚨Störung🚨 Derzeit steht eine #S2 Richtung Filderstadt mit einer Türstörung in Stg-Rohr. Es kommt auf den Linien #S1, #…",
"entity_mentions": [
{
"text": "@SBahn_Stuttgart",
"start": 1,
"end": 2,
"char_start": 3,
"char_end": 19,
"type": 13,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
},
{
"text": "Störung",
"start": 4,
"end": 5,
"char_start": 22,
"char_end": 29,
"type": 4,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
},
{
"text": "#S2",
"start": 9,
"end": 10,
"char_start": 50,
"char_end": 53,
"type": 7,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "17171"
}
]
},
{
"text": "Filderstadt",
"start": 11,
"end": 12,
"char_start": 63,
"char_end": 74,
"type": 6,
"entity_id": "2796535",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "-2796535"
},
{
"key": "osm_id",
"value": "2796535"
}
]
},
{
"text": "Türstörung",
"start": 14,
"end": 15,
"char_start": 85,
"char_end": 95,
"type": 4,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
},
{
"text": "Stg-Rohr",
"start": 16,
"end": 19,
"char_start": 99,
"char_end": 107,
"type": 8,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "NIL"
}
]
},
{
"text": "#S1",
"start": 25,
"end": 26,
"char_start": 133,
"char_end": 136,
"type": 7,
"entity_id": "NIL",
"refids": [
{
"key": "spreeDBReferenceId",
"value": "16703"
}
]
}
],
"event_mentions": [
{
"id": "r/0f748b57-63ec-4cb9-ab54-e35d29ac44f8",
"trigger": {
"text": "Störung",
"start": 4,
"end": 5,
"char_start": 22,
"char_end": 29
},
"arguments": [
{
"text": "#S2",
"start": 9,
"end": 10,
"char_start": 50,
"char_end": 53,
"role": 1,
"type": 7
}
],
"event_type": 5
}
],
"tokens": ["RT", "@SBahn_Stuttgart", ":", "🚨", "Störung", "🚨 ", "Derzeit", "steht", "eine", "#S2", "Richtung", "Filderstadt", "mit", "einer", "Türstörung", "in", "Stg", "-", "Rohr", ".", "Es", "kommt", "auf", "den", "Linien", "#S1", ",", "#", "…"],
"pos_tags": ["NN", "NN", "$.", "CARD", "NN", "CARD", "ADV", "VVFIN", "ART", "NN", "NN", "NE", "APPR", "ART", "NN", "APPR", "NE", "$[", "NE", "$.", "PPER", "VVFIN", "APPR", "ART", "NN", "CARD", "$,", "CARD", "$["],
"lemma": ["rt", "@sbahn_stuttgart", ":", "🚨", "störung", "🚨", "derzeit", "steht", "eine", "#s2", "richtung", "filderstadt", "mit", "einer", "türstörung", "in", "stg", "-", "rohr", ".", "es", "kommt", "auf", "den", "linien", "#s1", ",", "#", "..."],
"ner_tags": [0, 14, 0, 0, 5, 0, 0, 0, 0, 8, 0, 7, 0, 0, 5, 0, 9, 29, 29, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0]
}
```
### Data Fields
#### ner
- `id`: example identifier, a `string` feature.
- `tokens`: list of tokens, a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-date` (1), `I-date` (2), `B-disaster-type` (3), `I-disaster-type` (4), ...
#### el
- `id`: example identifier, a `string` feature.
- `text`: example text, a `string` feature.
- `entity_mentions`: a `list` of `struct` features.
- `text`: a `string` feature.
- `start`: token offset start, a `int32` feature.
- `end`: token offset end, a `int32` feature.
- `char_start`: character offset start, a `int32` feature.
- `char_end`: character offset end, a `int32` feature.
- `type`: a classification label, with possible values including `O` (0), `date` (1), `disaster-type` (2), `distance` (3), `duration` (4), `event-cause` (5), ...
- `entity_id`: Open Street Map ID, a `string` feature.
- `refids`: knowledge base ids, a `list` of `struct` features.
- `key`: name of the knowledge base, a `string` feature.
- `value`: identifier, a `string` feature.
#### re
- `id`: example identifier, a `string` feature.
- `text`: example text, a `string` feature.
- `tokens`: list of tokens, a `list` of `string` features.
- `entities`: a list of token spans, a `list` of `int32` featuress.
- `entity_roles`: a `list` of classification labels, with possible values including `no_arg` (0), `trigger` (1), `location` (2), `delay` (3), `direction` (4), ...
- `event_type`: a classification label, with possible values including `O` (0), `Accident` (1), `CanceledRoute` (2), `CanceledStop` (3), `Delay` (4), ...
- `entity_ids`: list of Open Street Map IDs, a `list` of `string` features.
#### ee
- `id`: example identifier, a `string` feature.
- `text`: example text, a `string` feature.
- `entity_mentions`: a `list` of `struct` features.
- `text`: a `string` feature.
- `start`: token offset start, a `int32` feature.
- `end`: token offset end, a `int32` feature.
- `char_start`: character offset start, a `int32` feature.
- `char_end`: character offset end, a `int32` feature.
- `type`: a classification label, with possible values including `O` (0), `date` (1), `disaster-type` (2), `distance` (3), `duration` (4), `event-cause` (5), ...
- `entity_id`: Open Street Map ID, a `string` feature.
- `refids`: knowledge base ids, a `list` of `struct` features.
- `key`: name of the knowledge base, a `string` feature.
- `value`: identifier, a `string` feature.
- `event_mentions`: a list of `struct` features.
- `id`: event identifier, a `string` feature.
- `trigger`: a `struct` feature.
- `text`: a `string` feature.
- `start`: token offset start, a `int32` feature.
- `end`: token offset end, a `int32` feature.
- `char_start`: character offset start, a `int32` feature.
- `char_end`: character offset end, a `int32` feature.
- `arguments`: a list of `struct` features.
- `text`: a `string` feature.
- `start`: token offset start, a `int32` feature.
- `end`: token offset end, a `int32` feature.
- `char_start`: character offset start, a `int32` feature.
- `char_end`: character offset end, a `int32` feature.
- `role`: a classification label, with possible values including `no_arg` (0), `trigger` (1), `location` (2), `delay` (3), `direction` (4), ...
- `type`: a classification label, with possible values including `O` (0), `date` (1), `disaster-type` (2), `distance` (3), `duration` (4), `event-cause` (5), ...
- `event_type`: a classification label, with possible values including `O` (0), `Accident` (1), `CanceledRoute` (2), `CanceledStop` (3), `Delay` (4), ...
- `tokens`: list of tokens, a `list` of `string` features.
- `pos_tags`: list of part-of-speech tags, a `list` of `string` features.
- `lemma`: list of lemmatized tokens, a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-date` (1), `I-date` (2), `B-disaster-type` (3), `I-disaster-type` (4), ...
### Data Splits
| | Train | Dev | Test |
|-----|-------|-----|------|
| NER | 2115 | 494 | 623 |
| EL | 2115 | 494 | 623 |
| RE | 1199 | 228 | 609 |
| EE | 788 | 152 | 484 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### 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",
}
```
### Contributions
|