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

  • JOKER 2023 ES

    Social
    Spanish , English
    Published in 2023
    4,235
    Tweets
    hate detection

  • EXIST-2023-ES

    Social
    Spanish , English
    Published in 2023
    4,653
    Tweets
    hate detection

  • HOPE-ES 2023

    Social
    Spanish
    Published in 2023
    2,062
    Tweets
    hate detection

  • HOMO-MEX 2023

    Social
    Spanish (Mexico)
    Published in 2023
    11,000
    Tweets
    hate detection

  • DA-VINCIS 2023

    Social
    Spanish (Mexico)
    Published in 2023
    4,731
    Tweets
    processing events

  • EXIST-2022-ES

    Spanish
    Published in 2022
    6,226
    Tweets
    hate detection

  • NewsCom-TOX

    Spanish
    Published in 2021
    4,359
    News comments
    hate detection

  • EXIST-2021-ES

    Spanish
    Published in 2021
    5,701
    Social networks
    hate detection

  • PAN-AP-2021-ES

    Social
    Spanish
    Published in 2021
    300
    Tweets
    hate detection

  • MeOffendES

    Spanish
    Published in 2021
    30,416
    Tweets Instagram comments Youtube comments
    hate detection

  • FACT

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

  • Mexican Aggressiveness Corpus

    Spanish (Mexico)
    Published in 2020
    10,475
    Tweets
    hate detection

  • HateEval-ES

    Spanish
    Published in 2019
    6,600
    Tweets
    hate detection

  • AMI-ES

    Spanish
    Published in 2018
    4,138
    Tweets
    hate detection

  • MEX-A3T-profiling

    Spanish (Mexico)
    Published in 2018
    5,000
    Tweets
    stylistic analysis, hate detection

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.