JOURNAL ARTICLE

ACSMKRHR at SemEval-2023 Task 10: Explainable Online Sexism Detection(EDOS)

Abstract

People are expressing their opinions online for a lot of years now.Although these opinions and comments provide people an opportunity of expressing their views, there is a lot of hate speech that can be found online.More specifically, sexist comments are very popular affecting and creating a negative impact on a lot of women and girls online.This paper describes the approaches of the SemEval-2023 Task 10 competition for Explainable Online Sexism Detection (EDOS).The task has been divided into 3 subtasks, introducing different classes of sexist comments.We have approached these tasks using the bert-cased and uncased models which are trained on the annotated dataset that has been provided in the competition.Task A provided the best F1 score of 80% on the test set, and tasks B and C provided 58% and 40% respectively.

Keywords:
Task (project management) Test (biology) Online assessment Competition (biology) Task analysis

Metrics

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Cited By
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FWCI (Field Weighted Citation Impact)
0
Refs
0.28
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Hate Speech and Cyberbullying Detection
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Sentiment Analysis and Opinion Mining
Physical Sciences →  Computer Science →  Artificial Intelligence
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