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    <abstract>This paper presents our work on the SemEval-2023 Task 10 Explainable Detection of Online Sexism (EDOS) 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 by 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\textbackslash% and 27.31\textbackslash% on the official blind test sets for tasks B and C, respectively. We, additionally, provide in-depth analysis highlighting model limitation and bias. We also present our attempts to understand the model’s behaviour based on our constructed lexicons. Our code and the resulting lexicons are open-sourced in our GitHub repository https://github.com/SirBadr/SemEval2022-Task10.</abstract>
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%0 Conference Proceedings
%T Lexicools at SemEval-2023 Task 10: Sexism Lexicon Construction via XAI
%A Nakwijit, Pakawat
%A Samir, Mahmoud
%A Purver, Matthew
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F nakwijit-etal-2023-lexicools
%X This paper presents our work on the SemEval-2023 Task 10 Explainable Detection of Online Sexism (EDOS) 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 by 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\textbackslash% and 27.31\textbackslash% on the official blind test sets for tasks B and C, respectively. We, additionally, provide in-depth analysis highlighting model limitation and bias. We also present our attempts to understand the model’s behaviour based on our constructed lexicons. Our code and the resulting lexicons are open-sourced in our GitHub repository https://github.com/SirBadr/SemEval2022-Task10.
%R 10.18653/v1/2023.semeval-1.4
%U https://aclanthology.org/2023.semeval-1.4
%U https://doi.org/10.18653/v1/2023.semeval-1.4
%P 23-43

Markdown (Informal)

[Lexicools at SemEval-2023 Task 10: Sexism Lexicon Construction via XAI](https://aclanthology.org/2023.semeval-1.4) (Nakwijit et al., SemEval 2023)

• Lexicools at SemEval-2023 Task 10: Sexism Lexicon Construction via XAI (Nakwijit et al., SemEval 2023)

ACL

• Pakawat Nakwijit, Mahmoud Samir, and Matthew Purver. 2023. Lexicools at SemEval-2023 Task 10: Sexism Lexicon Construction via XAI. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 23–43, Toronto, Canada. Association for Computational Linguistics.