Datasets

Below is information about Spanish textual data sets created with the goal of solving NLP tasks. In this case, these are collections of texts, generally enriched with annotations.

  • DIPROMATS-ES 2023

    Politics
    Spanish , English
    Published in 2023
    9,591
    Tweets
    text classification

  • HUHU 2023

    Social
    Spanish
    Published in 2023
    3,449
    Tweets
    processing humor

  • ClinAIS 2023

    Health
    Spanish
    Published in 2023
    1,038
    Clinical notes
    text classification

  • ADoBo

    Spanish
    Published in 2021
    Laws
    text classification

  • MEDDOPROF

    Health
    Spanish
    Published in 2021
    1,844
    Clinical records from journals
    text classification

  • HAHA

    Spanish
    Published in 2021
    36,000
    Tweets
    processing humor

  • IDAT-SP-EU

    Spanish
    Published in 2019
    Tweets
    processing humor

  • IDAT-SP-MEX

    Spanish (Mexico)
    Published in 2019
    3,000
    Tweets
    processing humor

  • IDAT-SP-CUBA

    Spanish (Cuba)
    Published in 2019
    3,000
    News comments
    processing humor

  • eHealth-KD 2019

    Spanish
    Published in 2019
    1,000
    Health
    information extraction

  • HAHA

    Spanish
    Published in 2019
    30,000
    Tweets
    processing humor

  • MEDDOCAN

    Health
    Spanish
    Published in 2019
    1,000
    Clinical case reports
    (named) entity recognition, information extraction

  • MLDoc-ES

    News
    Spanish
    Published in 2018
    14,458
    News
    text classification

  • DIANN-2018-ES

    Health
    Spanish
    Published in 2018
    500
    Abstracts scientific articles
    information extraction

  • DIANN-2018-EN

    Health
    English
    Published in 2018
    500
    Abstracts scientific articles
    information extraction

If you have published a result better than those on the list, send a message to odesia-comunicacion@lsi.uned.es indicating the result and the DOI of the article, along with a copy of it if it is not published openly.