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Dataset Card for "MobIE"
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 theBIO
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.
{
"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": [
{
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"value": "NIL"
}
]
},
{
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"start": 2,
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"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"
}
]
},
{
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"start": 5,
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"char_start": 39,
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"type": 0,
"entity_id": "NIL",
"refids": [
{
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"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, astring
feature.tokens
: list of tokens, alist
ofstring
features.ner_tags
: alist
of 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, astring
feature.text
: example text, astring
feature.entity_mentions
: alist
ofstruct
features.text
: astring
feature.start
: token offset start, aint32
feature.end
: token offset end, aint32
feature.char_start
: character offset start, aint32
feature.char_end
: character offset end, aint32
feature.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, astring
feature.refids
: knowledge base ids, alist
ofstruct
features.key
: name of the knowledge base, astring
feature.value
: identifier, astring
feature.
re
id
: example identifier, astring
feature.text
: example text, astring
feature.tokens
: list of tokens, alist
ofstring
features.entities
: a list of token spans, alist
ofint32
featuress.entity_roles
: alist
of 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, alist
ofstring
features.
ee
id
: example identifier, astring
feature.text
: example text, astring
feature.entity_mentions
: alist
ofstruct
features.text
: astring
feature.start
: token offset start, aint32
feature.end
: token offset end, aint32
feature.char_start
: character offset start, aint32
feature.char_end
: character offset end, aint32
feature.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, astring
feature.refids
: knowledge base ids, alist
ofstruct
features.key
: name of the knowledge base, astring
feature.value
: identifier, astring
feature.
event_mentions
: a list ofstruct
features.id
: event identifier, astring
feature.trigger
: astruct
feature.text
: astring
feature.start
: token offset start, aint32
feature.end
: token offset end, aint32
feature.char_start
: character offset start, aint32
feature.char_end
: character offset end, aint32
feature.
arguments
: a list ofstruct
features.text
: astring
feature.start
: token offset start, aint32
feature.end
: token offset end, aint32
feature.char_start
: character offset start, aint32
feature.char_end
: character offset end, aint32
feature.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, alist
ofstring
features.pos_tags
: list of part-of-speech tags, alist
ofstring
features.lemma
: list of lemmatized tokens, alist
ofstring
features.ner_tags
: alist
of 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",
}
Contributions
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