| dc.description.abstract | This paper presents our work on the SemEval-2023 Task 10 Explainable Detection of Online Sexism (EDOS) (Kirk et al., 2023) using lexicon-based models. Our approach consists of three main steps: lexicon construction based on Pointwise Mutual Information (PMI) and Shapley value, lexicon augmentation using an unannotated corpus and Large Language Models (LLMs), and, lastly, lexical incorporation for Bag-of-Word (BoW) logistic regression and fine-tuning LLMs. Our results demonstrate that our Shapley approach effectively produces a high-quality lexicon. We also show that simply counting the presence of certain words in our lexicons and comparing the count can outperform a BoW logistic regression in task B/C and fine-tuning BERT in task C. In the end, our classifier achieved F1-scores of 53.34% and 27.31% on the official blind test sets for tasks B and C, respectively. We, additionally, provide an in-depth analysis highlighting model limitations and bias. We also present our attempts to understand the model’s behavior based on our constructed lexicons. Our code and the resulting lexicons are open-sourced in our GitHub repository https://github.com/SirBadr/SemEval2023-Task10. | en_US |
