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.

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Year

  • HAHA

    Spanish
    Published in 2021
    36,000
    Tweets
    processing humor

  • CheckThat-ES

    Politics
    Spanish
    Published in 2021
    4,990
    Tweets
    fake news detection

  • CAPITEL-NER

    Spanish
    Published in 2020
    News
    (named) entity recognition

  • CAPITEL-UD

    Spanish
    Published in 2020
    News
    parsing

  • FACT

    Spanish , Spanish (Uruguay)
    Published in 2020
    News
    processing factuality, processing events

  • InterTASS 2020

    Spanish , Spanish (Chile) , Spanish (Costa Rica) , Spanish (Mexico) , Spanish (Peru) , Spanish (Uruguay)
    Published in 2020
    Tweets
    sentiment analysis

  • EmoEVENT

    Spanish , Spanish (Chile) , Spanish (Costa Rica) , Spanish (Mexico) , Spanish (Peru) , Spanish (Uruguay)
    Published in 2020
    8,409
    Tweets
    sentiment analysis

  • SentiMix-Spanglish

    Spanish
    Published in 2020
    18,789
    Tweets
    sentiment analysis

  • CodiEsp

    Spanish
    Published in 2020
    1,000
    Clinical case reports
    text indexing

  • PAN-AP-2020-ES

    Spanish
    Published in 2020
    500
    Tweets
    fake news detection

  • IDAT-SP-EU

    Spanish
    Published in 2019
    Tweets
    processing humor

  • NEGES

    Spanish
    Published in 2019
    400
    Reviews
    sentiment analysis, processing negation

  • eHealth-KD 2019

    Spanish
    Published in 2019
    1,000
    Health
    information extraction

  • FACT

    Spanish , Spanish (Uruguay)
    Published in 2019
    News
    processing factuality

  • HAHA

    Spanish
    Published in 2019
    30,000
    Tweets
    processing humor

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.