

# 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
  },
}

```

multilinguality:multilingual language:en language:zh language:ja license:cc-by-4.0 region:us
{"language": ["en", "zh", "ja"], "license": "cc-by-4.0", "multilinguality": "multilingual", "pretty_name": "NTCIR-13 MedWeb", "bigbio_language": ["English", "Chinese", "Japanese"], "bigbio_license_shortname": "CC_BY_4p0", "homepage": "http://research.nii.ac.jp/ntcir/permission/ntcir-13/perm-en-MedWeb.html", "bigbio_pubmed": false, "bigbio_public": false, "bigbio_tasks": ["TRANSLATION", "TEXT_CLASSIFICATION"]}
TAGS
#multilinguality-multilingual #language-English #language-Chinese #language-Japanese #license-cc-by-4.0 #region-us 



# Dataset Card for NTCIR-13 MedWeb

## Dataset Description

- Homepage: URL
- 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:
URL

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.







# Dataset Card for NTCIR-13 MedWeb ## Dataset Description

- Homepage: URL
- 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:
URL

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.
TAGS
#multilinguality-multilingual #language-English #language-Chinese #language-Japanese #license-cc-by-4.0 #region-us 
 # Dataset Card for NTCIR-13 MedWeb ## Dataset Description

- Homepage: URL
- 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:
URL

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.
passage: TAGS
#multilinguality-multilingual #language-English #language-Chinese #language-Japanese #license-cc-by-4.0 #region-us 
# Dataset Card for NTCIR-13 MedWeb## Dataset Description

- Homepage: URL
- 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:
URL

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.