README.md

metadata

dataset_info:
  - config_name: ee
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
      - name: entity_mentions
        list:
          - name: id
            dtype: string
          - name: text
            dtype: string
          - name: start
            dtype: int64
          - name: end
            dtype: int64
          - name: char_start
            dtype: int64
          - name: char_end
            dtype: int64
          - name: type
            dtype: string
      - name: event_mentions
        list:
          - name: id
            dtype: string
          - name: trigger
            struct:
              - name: text
                dtype: string
              - name: start
                dtype: int64
              - name: end
                dtype: int64
              - name: char_start
                dtype: int64
              - name: char_end
                dtype: int64
          - name: arguments
            list:
              - name: text
                dtype: string
              - name: start
                dtype: int64
              - name: end
                dtype: int64
              - name: char_start
                dtype: int64
              - name: char_end
                dtype: int64
              - name: role
                dtype: string
              - name: type
                dtype: string
          - name: event_type
            dtype: string
      - name: tokens
        sequence: string
      - name: pos_tags
        sequence: string
      - name: lemma
        sequence: string
      - name: ner_tags
        sequence: string
    splits:
      - name: train
        num_bytes: 6532239
        num_examples: 1861
      - name: validation
        num_bytes: 792697
        num_examples: 228
      - name: test
        num_bytes: 802322
        num_examples: 230
    download_size: 3171788
    dataset_size: 8127258
  - config_name: ner
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence: string
    splits:
      - name: train
        num_bytes: 2062754
        num_examples: 1861
      - name: validation
        num_bytes: 250635
        num_examples: 228
      - name: test
        num_bytes: 255164
        num_examples: 230
    download_size: 736425
    dataset_size: 2568553
  - config_name: re
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: entities
        sequence:
          sequence: int64
      - name: entity_roles
        sequence: string
      - name: entity_types
        sequence: string
      - name: event_type
        dtype: string
      - name: entity_ids
        sequence: string
    splits:
      - name: train
        num_bytes: 2116771
        num_examples: 1007
      - name: validation
        num_bytes: 265248
        num_examples: 129
      - name: test
        num_bytes: 238094
        num_examples: 128
    download_size: 801404
    dataset_size: 2620113
configs:
  - config_name: ee
    data_files:
      - split: train
        path: ee/train-*
      - split: validation
        path: ee/validation-*
      - split: test
        path: ee/test-*
  - config_name: ner
    data_files:
      - split: train
        path: ner/train-*
      - split: validation
        path: ner/validation-*
      - split: test
        path: ner/test-*
  - config_name: re
    data_files:
      - split: train
        path: re/train-*
      - split: validation
        path: re/validation-*
      - split: test
        path: re/test-*
license: cc-by-4.0
task_categories:
  - text-classification
  - token-classification
language:
  - de
tags:
  - finance
  - relation-extraction
  - event-extraction
  - traffic
  - industry
pretty_name: SmartData Corpus
size_categories:
  - 1K<n<10K

Dataset Card for SmartData Corpus

Dataset Description

• Repository: https://github.com/dfki-nlp/smartdata-corpus
• Paper: A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events

Dataset Summary

SmartData Corpus is a German-language dataset which is human-annotated with entity types and a set of 15 traffic- and industry-related n-ary relations and events, such as accidents, traffic jams, acquisitions, and strikes. The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and railway companies.

This version of the dataset loader provides configurations for:

• Named Entity Recognition (ner): NER tags use the BIO tagging scheme
• 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/smartdata-corpus and https://www.dfki.de/web/forschung/projekte-publikationen/publikation/9427/.

Supported Tasks and Leaderboards

• Tasks: Named Entity Recognition, n-ary Relation Extraction, Event Extraction
• Leaderboards:

Languages

German

Dataset Structure

Data Instances

ner

An example of 'train' looks as follows.

{
  "id": "671734738147758080",
  "tokens": ["A1", "Zwischen", "AS", "Munsbach", "und", "AS", "Flaxweiler", "Bauarbeiten", ",", "rechter", "Fahrstreifen", "gesperrt", ",", "Verkehrsbehinderung", ",", "Dauer", ":", "02.12.2015", "...", "#ACL_A1"],
  "ner_tags": ["B-LOCATION_STREET", "O", "B-LOCATION", "I-LOCATION", "O", "B-LOCATION", "I-LOCATION", "O", "O", "O", "O", "O", "O", "B-TRIGGER", "O", "O", "O", "B-DATE", "O", "B-LOCATION_STREET"]
}

re

An example of 'train' looks as follows.

{
  "id": "671734738147758080_0",
  "tokens": ["A1", "Zwischen", "AS", "Munsbach", "und", "AS", "Flaxweiler", "Bauarbeiten", ",", "rechter", "Fahrstreifen", "gesperrt", ",", "Verkehrsbehinderung", ",", "Dauer", ":", "02.12.2015", "...", "#ACL_A1"],
  "entities": [[0, 1], [2, 4], [5, 7], [13, 14], [17, 18], [19, 20]],
  "entity_roles": ["location", "start_loc", "end_loc", "trigger", "end_date", "no_arg"],
  "entity_types": ["LOCATION_STREET", "LOCATION", "LOCATION", "TRIGGER", "DATE", "LOCATION_STREET"],
  "event_type": "Obstruction",
  "entity_ids": ["c/ac611f0a-d610-4ab2-9ddf-00132d9374b5", "c/3e01d530-58c4-4f47-9ab3-082a58e8299b", "c/cb6975e8-4409-4bdf-a491-de398b3c3263", "c/684a0ccd-06ff-4a8f-a90f-bdef169077dc", "c/166acddb-0f4d-48eb-98f6-a8b490f2e578", "c/ca3befa0-92da-4ff9-b34d-ec351854cdda"]
}

ee

An example of 'train' looks as follows.

{
  "id": "671734738147758080",
  "text": "A1 Zwischen AS Munsbach und AS Flaxweiler Bauarbeiten, rechter Fahrstreifen gesperrt, Verkehrsbehinderung, Dauer: 02.12.2015... #ACL_A1\n",
  "entity_mentions": [
    {"id": "c/ac611f0a-d610-4ab2-9ddf-00132d9374b5", "text": "A1", "start": 0, "end": 1, "char_start": 0, "char_end": 2, "type": "LOCATION_STREET"},
    {"id": "c/3e01d530-58c4-4f47-9ab3-082a58e8299b", "text": "AS Munsbach", "start": 2, "end": 4, "char_start": 12, "char_end": 23, "type": "LOCATION"},
    {"id": "c/cb6975e8-4409-4bdf-a491-de398b3c3263", "text": "AS Flaxweiler", "start": 5, "end": 7, "char_start": 28, "char_end": 41, "type": "LOCATION"},
    {"id": "c/684a0ccd-06ff-4a8f-a90f-bdef169077dc", "text": "Verkehrsbehinderung", "start": 13, "end": 14, "char_start": 86, "char_end": 105, "type": "TRIGGER"},
    {"id": "c/166acddb-0f4d-48eb-98f6-a8b490f2e578", "text": "02.12.2015", "start": 17, "end": 18, "char_start": 114, "char_end": 124, "type": "DATE"},
    {"id": "c/ca3befa0-92da-4ff9-b34d-ec351854cdda", "text": "#ACL_A1", "start": 19, "end": 20, "char_start": 128, "char_end": 135, "type": "LOCATION_STREET"}
  ],
  "event_mentions": [
    {
      "id": "r/802a82c2-c214-4429-b9f1-bf56e46674ee",
      "trigger": {
        "text": "Verkehrsbehinderung", "start": 13, "end": 14, "char_start": 86, "char_end": 105
      },
      "arguments": [
        {"text": "02.12.2015", "start": 17, "end": 18, "char_start": 114, "char_end": 124, "role": "end_date", "type": "date"},
        {"text": "AS Flaxweiler", "start": 5, "end": 7, "char_start": 28, "char_end": 41, "role": "end_loc", "type": "location"},
        {"text": "AS Munsbach", "start": 2, "end": 4, "char_start": 12, "char_end": 23, "role": "start_loc", "type": "location"},
        {"text": "A1", "start": 0, "end": 1, "char_start": 0, "char_end": 2, "role": "location", "type": "location-street"}
      ],
      "event_type": "Obstruction"
    }
  ],
  "tokens": ["A1", "Zwischen", "AS", "Munsbach", "und", "AS", "Flaxweiler", "Bauarbeiten", ",", "rechter", "Fahrstreifen", "gesperrt", ",", "Verkehrsbehinderung", ",", "Dauer", ":", "02.12.2015", "...", "#ACL_A1"],
  "pos_tags": ["CARD", "APPR", "NE", "NE", "KON", "NE", "NE", "NN", "$,", "ADJA", "NN", "VVPP", "$,", "NN", "$,", "NN", "$.", "CARD", "$[", "CARD"],
  "lemma": ["a1", "zwischen", "as", "munsbach", "und", "as", "flaxweiler", "bauarbeiten", ",", "rechter", "fahrstreifen", "gesperrt", ",", "verkehrsbehinderung", ",", "dauer", ":", "02.12.2015", "...", "#acl_a1"],
  "ner_tags": ["B-LOCATION_STREET", "O", "B-LOCATION", "I-LOCATION", "O", "B-LOCATION", "I-LOCATION", "O", "O", "O", "O", "O", "O", "B-TRIGGER", "O", "O", "O", "B-DATE", "O", "B-LOCATION_STREET"]
}

Data Fields

ner

• id: example identifier, a string feature.
• tokens: list of tokens, a list of string features.
• ner_tags: list of NER tags, a list of string features.

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 int64 features.
• entity_roles: a list of entity roles, a list of string features.
• event_type: the event type, a string feature.
• entity_ids: list of entity 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 int64 feature.
  • end: token offset end, a int64 feature.
  • char_start: character offset start, a int64 feature.
  • char_end: character offset end, a int64 feature.
  • type: entity type, a string feature.
  • id: entity id, 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 int64 feature.
    • end: token offset end, a int64 feature.
    • char_start: character offset start, a int64 feature.
    • char_end: character offset end, a int64 feature.
  • arguments: a list of struct features.
    • text: a string feature.
    • start: token offset start, a int64 feature.
    • end: token offset end, a int64 feature.
    • char_start: character offset start, a int64 feature.
    • char_end: character offset end, a int64 feature.
    • role: role of the argument, a string feature.
    • type: entity type of the argument, a string feature.
  • event_type: a classification label, a string feature.
• 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 NER tags, a list of string features.

Licensing Information

CC BY-SA 4.0 license

Citation Information

BibTeX:

@InProceedings{SCHIERSCH18.85,
  author = {Martin Schiersch and Veselina Mironova and Maximilian Schmitt and Philippe Thomas and Aleksandra Gabryszak and Leonhard Hennig},
  title = "{A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
  }

APA:

• Schiersch, M., Mironova, V., Schmitt, M., Thomas, P., Gabryszak, A., & Hennig, L. (2018). A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events. In N. Calzolari (Conference chair), K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis, & T. Tokunaga (Eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (pp. Unknown). Miyazaki, Japan: European Language Resources Association (ELRA). ISBN: 979-10-95546-00-9.

Contributions