Datasets:

DFKI-SLT
/

mobie

	---
	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:
	- text-classification
	- token-classification
	task_ids:
	- named-entity-recognition
	- entity-linking-classification
	- multi-class-classification
	paperswithcode_id: mobie
	pretty_name: MobIE
	tags:
	- structure-prediction
	- mobility
	- relation extraction
	- entity linking
	- named entity recognition
	dataset_info:
	- config_name: ee
	features:
	- name: id
	dtype: string
	- name: text
	dtype: string
	- name: entity_mentions
	list:
	- name: text
	dtype: string
	- name: start
	dtype: int32
	- name: end
	dtype: int32
	- name: char_start
	dtype: int32
	- name: char_end
	dtype: int32
	- name: type
	dtype:
	class_label:
	names:
	'0': O
	'1': date
	'2': disaster-type
	'3': distance
	'4': duration
	'5': event-cause
	'6': location
	'7': location-city
	'8': location-route
	'9': location-stop
	'10': location-street
	'11': money
	'12': number
	'13': organization
	'14': organization-company
	'15': org-position
	'16': percent
	'17': person
	'18': set
	'19': time
	'20': trigger
	- name: entity_id
	dtype: string
	- name: refids
	list:
	- name: key
	dtype: string
	- name: value
	dtype: string
	- name: event_mentions
	list:
	- name: id
	dtype: string
	- name: trigger
	struct:
	- name: text
	dtype: string
	- name: start
	dtype: int32
	- name: end
	dtype: int32
	- name: char_start
	dtype: int32
	- name: char_end
	dtype: int32
	- name: arguments
	list:
	- name: text
	dtype: string
	- name: start
	dtype: int32
	- name: end
	dtype: int32
	- name: char_start
	dtype: int32
	- name: char_end
	dtype: int32
	- name: role
	dtype:
	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: type
	dtype:
	class_label:
	names:
	'0': O
	'1': date
	'2': disaster-type
	'3': distance
	'4': duration
	'5': event-cause
	'6': location
	'7': location-city
	'8': location-route
	'9': location-stop
	'10': location-street
	'11': money
	'12': number
	'13': organization
	'14': organization-company
	'15': org-position
	'16': percent
	'17': person
	'18': set
	'19': time
	'20': 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
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	num_examples: 2115
	- name: test
	num_bytes: 1334445
	num_examples: 623
	- name: validation
	num_bytes: 827821
	num_examples: 494
	download_size: 1891736
	dataset_size: 5920006
	- config_name: el
	features:
	- name: id
	dtype: string
	- name: text
	dtype: string
	- name: entity_mentions
	list:
	- name: text
	dtype: string
	- name: start
	dtype: int32
	- name: end
	dtype: int32
	- name: char_start
	dtype: int32
	- name: char_end
	dtype: int32
	- name: type
	dtype:
	class_label:
	names:
	'0': O
	'1': date
	'2': disaster-type
	'3': distance
	'4': duration
	'5': event-cause
	'6': location
	'7': location-city
	'8': location-route
	'9': location-stop
	'10': location-street
	'11': money
	'12': number
	'13': organization
	'14': organization-company
	'15': org-position
	'16': percent
	'17': person
	'18': set
	'19': time
	'20': trigger
	- name: entity_id
	dtype: string
	- name: refids
	list:
	- name: key
	dtype: string
	- name: value
	dtype: string
	splits:
	- name: train
	num_bytes: 1487615
	num_examples: 2115
	- name: test
	num_bytes: 557349
	num_examples: 623
	- name: validation
	num_bytes: 329567
	num_examples: 494
	download_size: 819444
	dataset_size: 2374531
	- 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
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	num_examples: 2115
	- name: test
	num_bytes: 354244
	num_examples: 623
	- name: validation
	num_bytes: 251031
	num_examples: 494
	download_size: 486201
	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': O
	'1': date
	'2': disaster-type
	'3': distance
	'4': duration
	'5': event-cause
	'6': location
	'7': location-city
	'8': location-route
	'9': location-stop
	'10': location-street
	'11': money
	'12': number
	'13': organization
	'14': organization-company
	'15': org-position
	'16': percent
	'17': person
	'18': set
	'19': time
	'20': 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: 342446
	dataset_size: 1728794
	configs:
	- config_name: ee
	data_files:
	- split: train
	path: ee/train-*
	- split: test
	path: ee/test-*
	- split: validation
	path: ee/validation-*
	- config_name: el
	data_files:
	- split: train
	path: el/train-*
	- split: test
	path: el/test-*
	- split: validation
	path: el/validation-*
	- config_name: ner
	data_files:
	- split: train
	path: ner/train-*
	- split: test
	path: ner/test-*
	- split: validation
	path: ner/validation-*
	default: true
	- config_name: re
	data_files:
	- split: train
	path: re/train-*
	- split: test
	path: re/test-*
	- split: validation
	path: re/validation-*
	---

	# 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: 5.9 MB
	- Total amount of disk used: 14.1 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 \|
	\| EL \| 2115 \| 494 \| 623 \|

	## 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