Datasets:
metadata
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
num_bytes: 3757740
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
num_bytes: 1112606
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 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: 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 theBIOtagging 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.
{
"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.
{
"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.
{
"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.
{
"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, astringfeature.tokens: list of 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), ...
el
id: example identifier, astringfeature.text: example text, astringfeature.entity_mentions: alistofstructfeatures.text: astringfeature.start: token offset start, aint32feature.end: token offset end, aint32feature.char_start: character offset start, aint32feature.char_end: character offset end, aint32feature.type: a classification label, with possible values includingO(0),date(1),disaster-type(2),distance(3),duration(4),event-cause(5), ...entity_id: Open Street Map ID, astringfeature.refids: knowledge base ids, alistofstructfeatures.key: name of the knowledge base, astringfeature.value: identifier, astringfeature.
re
id: example identifier, astringfeature.text: example text, astringfeature.tokens: list of tokens, alistofstringfeatures.entities: a list of token spans, alistofint32featuress.entity_roles: alistof classification labels, with possible values includingno_arg(0),trigger(1),location(2),delay(3),direction(4), ...event_type: a classification label, with possible values includingO(0),Accident(1),CanceledRoute(2),CanceledStop(3),Delay(4), ...entity_ids: list of Open Street Map IDs, alistofstringfeatures.
ee
id: example identifier, astringfeature.text: example text, astringfeature.entity_mentions: alistofstructfeatures.text: astringfeature.start: token offset start, aint32feature.end: token offset end, aint32feature.char_start: character offset start, aint32feature.char_end: character offset end, aint32feature.type: a classification label, with possible values includingO(0),date(1),disaster-type(2),distance(3),duration(4),event-cause(5), ...entity_id: Open Street Map ID, astringfeature.refids: knowledge base ids, alistofstructfeatures.key: name of the knowledge base, astringfeature.value: identifier, astringfeature.
event_mentions: a list ofstructfeatures.id: event identifier, astringfeature.trigger: astructfeature.text: astringfeature.start: token offset start, aint32feature.end: token offset end, aint32feature.char_start: character offset start, aint32feature.char_end: character offset end, aint32feature.
arguments: a list ofstructfeatures.text: astringfeature.start: token offset start, aint32feature.end: token offset end, aint32feature.char_start: character offset start, aint32feature.char_end: character offset end, aint32feature.role: a classification label, with possible values includingno_arg(0),trigger(1),location(2),delay(3),direction(4), ...type: a classification label, with possible values includingO(0),date(1),disaster-type(2),distance(3),duration(4),event-cause(5), ...
event_type: a classification label, with possible values includingO(0),Accident(1),CanceledRoute(2),CanceledStop(3),Delay(4), ...
tokens: list of tokens, alistofstringfeatures.pos_tags: list of part-of-speech tags, alistofstringfeatures.lemma: list of lemmatized 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 | |
|---|---|---|---|
| NER | 2115 | 494 | 623 |
| EL | 2115 | 494 | 623 |
| RE | 1199 | 228 | 609 |
| EL | 2115 | 494 | 623 |
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",
}