README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - found
language:
  - de
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
paperswithcode_id: nosta-d-named-entity-annotation-for-german
pretty_name: GermEval14

Dataset Card for "germeval_14"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • 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
  • Contributions

Dataset Description

• Homepage: https://sites.google.com/site/germeval2014ner/
• Repository: More Information Needed
• Paper: https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf
• Point of Contact: Darina Benikova
• Size of downloaded dataset files: 9.81 MB
• Size of the generated dataset: 17.19 MB
• Total amount of disk used: 27.00 MB

Dataset Summary

The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties: - The data was sampled from German Wikipedia and News Corpora as a collection of citations. - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens. - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]].

Supported Tasks and Leaderboards

More Information Needed

Languages

German

Dataset Structure

Data Instances

germeval_14

• Size of downloaded dataset files: 9.81 MB
• Size of the generated dataset: 17.19 MB
• Total amount of disk used: 27.00 MB

An example of 'train' looks as follows. This example was too long and was cropped:

{
    "id": "11",
    "ner_tags": [13, 14, 14, 14, 14, 0, 0, 0, 0, 0, 0, 0, 19, 20, 13, 0, 1, 0, 0, 0, 0, 0, 19, 20, 20, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    "nested_ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    "source": "http://de.wikipedia.org/wiki/Liste_von_Filmen_mit_homosexuellem_Inhalt [2010-01-11] ",
    "tokens": "[\"Scenes\", \"of\", \"a\", \"Sexual\", \"Nature\", \"(\", \"GB\", \"2006\", \")\", \"-\", \"Regie\", \":\", \"Ed\", \"Blum\", \"Shortbus\", \"(\", \"USA\", \"2006..."
}

Data Fields

The data fields are the same among all splits.

germeval_14

• id: a string feature.
• source: a string feature.
• tokens: a list of string features.
• ner_tags: a list of classification labels, with possible values including O (0), B-LOC (1), I-LOC (2), B-LOCderiv (3), I-LOCderiv (4).
• nested_ner_tags: a list of classification labels, with possible values including O (0), B-LOC (1), I-LOC (2), B-LOCderiv (3), I-LOCderiv (4).

Data Splits

name	train	validation	test
germeval_14	24000	2200	5100

Additional Information

Licensing Information

CC BY-SA 4.0 license

Citation Information

@inproceedings{benikova-etal-2014-nosta,
    title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset},
    author = {Benikova, Darina  and
      Biemann, Chris  and
      Reznicek, Marc},
    booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
    month = {may},
    year = {2014},
    address = {Reykjavik, Iceland},
    publisher = {European Language Resources Association (ELRA)},
    url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/276_Paper.pdf},
    pages = {2524--2531},
}

Contributions

Thanks to @thomwolf, @jplu, @lewtun, @lhoestq, @stefan-it, @mariamabarham for adding this dataset.