---
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-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