@inproceedings{nakwijit-etal-2023-lexicools,
    title = "Lexicools at {S}em{E}val-2023 Task 10: Sexism Lexicon Construction via {XAI}",
    author = "Nakwijit, Pakawat  and
      Samir, Mahmoud  and
      Purver, Matthew",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.semeval-1.4/",
    doi = "10.18653/v1/2023.semeval-1.4",
    pages = "23--43",
    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 \url{https://github.com/SirBadr/SemEval2022-Task10}."
}