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 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.

{ 
  "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, 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

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

CC BY-SA 4.0 license

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