Datasets:
kor_ner

Task Categories: structure-prediction
Languages: ko
Multilinguality: monolingual
Size Categories: 1K<n<10K
Licenses: mit
Language Creators: other
Annotations Creators: expert-generated
Source Datasets: original

Dataset Card for [Dataset Name]

Dataset Summary

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Languages

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Dataset Structure

Data Instances

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Data Fields

Each row consists of the following fields:

  • text: The full text, as is
  • annot_text: Annotated text including POS-tagged information
  • tokens: An ordered list of tokens from the full text
  • pos_tags: Part-of-speech tags for each token
  • ner_tags: Named entity recognition tags for each token

Note that by design, the length of tokens, pos_tags, and ner_tags will always be identical.

pos_tags corresponds to the list below:

['SO', 'SS', 'VV', 'XR', 'VCP', 'JC', 'VCN', 'JKB', 'MM', 'SP', 'XSN', 'SL', 'NNP', 'NP', 'EP', 'JKQ', 'IC', 'XSA', 'EC', 'EF', 'SE', 'XPN', 'ETN', 'SH', 'XSV', 'MAG', 'SW', 'ETM', 'JKO', 'NNB', 'MAJ', 'NNG', 'JKV', 'JKC', 'VA', 'NR', 'JKG', 'VX', 'SF', 'JX', 'JKS', 'SN']

ner_tags correspond to the following:

["I", "O", "B_OG", "B_TI", "B_LC", "B_DT", "B_PS"]

The prefix B denotes the first item of a phrase, and an I denotes any non-initial word. In addition, OG represens an organization; TI, time; DT, date, and PS, person.

Data Splits

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Dataset Creation

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

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Citation Information

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Contributions

Thanks to @jaketae for adding this dataset.

Models trained or fine-tuned on kor_ner

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