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Overview of DA-VINCIS at IberLEF 2023: Detection of Aggressive and Violent Incidents from Social Media in Spanish
Overview of DA-VINCIS at IberLEF 2023: Detection of Aggressive and Violent Incidents from Social Media in Spanish
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Overview of DA-VINCIS at IberLEF 2023: Detection of Aggressive and Violent Incidents from Social Media in Spanish
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Overview of DA-VINCIS at IberLEF 2023: Detection of Aggressive and Violent Incidents from Social Media in Spanish
Overview of DA-VINCIS at IberLEF 2023: Detection of Aggressive and Violent Incidents from Social Media in Spanish

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Overview of DA-VINCIS at IberLEF 2023: Detection of Aggressive and Violent Incidents from Social Media in Spanish
Overview of DA-VINCIS at IberLEF 2023: Detection of Aggressive and Violent Incidents from Social Media in Spanish
Journal Article

Overview of DA-VINCIS at IberLEF 2023: Detection of Aggressive and Violent Incidents from Social Media in Spanish

2023
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Overview
In this paper, we present the overview of the DA-VINCIS 2023 shared task which was organized at IberLEF 2023 and co-located in the framework of the 39th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2023). The main aim of this task is to promote the research on developing automatic solutions for detecting violent events in social networks. Two subtasks were considered: (i) A binary classification task aimed to determine whether or not a tweet is about a violent incident; and (ii) A multi-label multi-class classification task in which the category(ies) of a violent incident must be identified. A multimodal manual annotated corpus comprising both tweets and images associated to them was provided to the participants. A total of 15 systems were submitted for the final evaluation phase. Competitive results were obtained for both subtasks, the higher ones were in the binary classification task. Corpora and results are available at the shared task website at https://codalab.lisn.upsaclay.fr/competitions/11312.
Publisher
Sociedad Española para el Procesamiento del Lenguaje Natural