@inproceedings{hacohen-kerner-etal-2022-jct,
    title = "{JCT} at {S}em{E}val-2022 Task 4-A: Patronism Detection in Posts Written in {E}nglish using Preprocessing Methods and various Machine Leaerning Methods",
    author = "HaCohen-Kerner, Yaakov  and
      Meyrowitsch, Ilan  and
      Fchima, Matan",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2022.semeval-1.72/",
    doi = "10.18653/v1/2022.semeval-1.72",
    pages = "519--524",
    abstract = "In this paper, we describe our submissions to SemEval-2022 subtask 4-A - {\textquotedblleft}Patronizing and Condescending Language Detection: Binary Classification{\textquotedblright}. We developed different models for this subtask. We applied 11 supervised machine learning methods and 9 preprocessing methods. Our best submission was a model we built with BertForSequenceClassification. Our experiments indicate that pre-processing stage is a must for a successful model. The dataset for Subtask 1 is highly imbalanced dataset. The f1-scores on the oversampled imbalanced training dataset were higher the results on the original training dataset."
}