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Dataset Card for 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.
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
},
}
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