upload hub_repos/ntcir_13_medweb/README.md to hub from bigbio repo

README.md

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+
+---
+language: 
+- en
+- zh
+- ja
+license: cc-by-4.0
+license_bigbio_shortname: CC_BY_4p0
+pretty_name: NTCIR-13 MedWeb
+---
+
+
+# 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:** Translation, Text Classification
+
+
+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
+  },
+}
+
+```