CoolFace
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asus-aics/ntcir_13_medweb

NTCIR-13 MedWeb (Medical Natural Language Processing for Web Document) task requires to perform a multi-label classification that labels for eight diseases/symptoms must be assigned to each tweet. Given pseudo-tweets, the output are Positive:p or Negative:n labels for eight diseases/symptoms. The achievements of this task can almost be directly applied to a fundamental engine for actual applications. This task provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering three languages (Japanese, English, and Chinese), and annotated with eight labels such as influenza, diarrhea/stomachache, hay fever, cough/sore throat, headache, fever, runny nose, and cold. For more information, see: http://research.nii.ac.jp/ntcir/permission/ntcir-13/perm-en-MedWeb.html As this dataset also provides a parallel corpus of pseudo-tweets for english, japanese and chinese it can also be used to train translation models between these three languages.

sourceHugging Facecc-by-4.0updated 2y agoView on Hugging Face
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Dataset Card

language:

  • —en
  • —zh
  • —ja bigbio_language:
  • —English
  • —Chinese
  • —Japanese license: cc-by-4.0 multilinguality: multilingual bigbiolicenseshortname: CCBY4p0 prettyname: NTCIR-13 MedWeb homepage: http://research.nii.ac.jp/ntcir/permission/ntcir-13/perm-en-MedWeb.html bigbiopubmed: False bigbiopublic: False bigbiotasks:
  • —TRANSLATION
  • —TEXT_CLASSIFICATION ---

Dataset Card for NTCIR-13 MedWeb

Dataset Description

  • —Homepage: http://research.nii.ac.jp/ntcir/permission/ntcir-13/perm-en-MedWeb.html
  • —Pubmed: False
  • —Public: False
  • —Tasks: TRANSL,TXTCLASS

NTCIR-13 MedWeb (Medical Natural Language Processing for Web Document) task requires to perform a multi-label classification that labels for eight diseases/symptoms must be assigned to each tweet. Given pseudo-tweets, the output are Positive:p or Negative:n labels for eight diseases/symptoms. The achievements of this task can almost be directly applied to a fundamental engine for actual applications.

This task provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering three languages (Japanese, English, and Chinese), and annotated with eight labels such as influenza, diarrhea/stomachache, hay fever, cough/sore throat, headache, fever, runny nose, and cold.

For more information, see: http://research.nii.ac.jp/ntcir/permission/ntcir-13/perm-en-MedWeb.html

As this dataset also provides a parallel corpus of pseudo-tweets for english, japanese and chinese it can also be used to train translation models between these three languages.

Citation Information

@article{wakamiya2017overview,
  author    = {Shoko Wakamiya, Mizuki Morita, Yoshinobu Kano, Tomoko Ohkuma and Eiji Aramaki},
  title     = {Overview of the NTCIR-13 MedWeb Task},
  journal   = {Proceedings of the 13th NTCIR Conference on Evaluation of Information Access Technologies (NTCIR-13)},
  year      = {2017},
  url       = {
    http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings13/pdf/ntcir/01-NTCIR13-OV-MEDWEB-WakamiyaS.pdf
  },
}