Publikationen

Autor	Fan, Shiying; Frick, Raphael Antonius; Steinebach, Martin
Datum	2024
Art	Conference Paper
Abstrakt	The dissemination of inappropriate speeches, such as sexist language, on social media has a negative impact on internet users. To promote the technical development of automatic sexism detection, the EXIST lab has been engaged in this field for the past three years. This paper presents a technical report from the FraunhoferSIT team participating in the EXIST shared task for 2024. To address the issue of detecting sexism in tweets, we have experimented with ensemble learning algorithms. Additionally, we implemented a data augmentation method through synonym replacement using rule-based techniques and language models to increase the size of the training data. We participated in tasks 1 to 3. In general, the proposed system did not demonstrate a competitive performance among other systems in the challenge. However, it was observed that it exhibited a better performance in regression tasks compared to its performance in classification tasks.
Konferenz	25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024
ISSN	16130073
Url	https://publica.fraunhofer.de/handle/publica/510334

FraunhoferSIT@EXIST2024: Leveraging Stacking Ensemble Learning for Sexism Detection