DFKI-SLT/smartdata-corpus

Dataset Card for SmartData Corpus

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