PCL is often involuntary and unconscious and the authors using such language are usually trying to help the communities in need, by raising awareness, moving the audience to action or standing for the rights of the under-represented. But PCL can potentially be very harmful, as it feeds stereotypes, routinizes discrimination and drives to greater exclusion. In our project, we applied classic machine learning, and deep learning models to classify the documents with PCL.
- Logistic Regression (LR)
- Random Forests (RF)
- K Nearest Neighbor (KNN)
- Decision Tree (DT)
- Naive Bayes (NB)
- Suport Vector Machine (SVM)
- LightGBM (LGBM)
- XGBoost (XGB)
- LSTM-based models
- Vanilla BiLSTM
- Stacked BiLSTM
- BiLSTM with attention
- CNN
- BERT
- GPT
