This is the repository for the final assignment of the NLP class followed at the University of Antwerp. The assignment follows the OffensEval 2019 shared task competition with a focus on the sub-task A, aiming to classify offensive text.

The folder data contains the training and test sets, as well as the processed datasets required to reproduce the experiments. Information about data and the processes of annotation can be found in the information folder.

The final paper of this research explaining the process and results can be found in the main directory.

The predictions for each reported model can be found in the predictions directory.

In the notebooks directory there are different Jupyter Notebooks available (written in Python):

TrainingData_Exploration_Preprocessing

This notebook reads, cleans, pre-processes and prepares input datasets for training SVM and BERT models.

TestData_Preprocessing_Exploration

Following the same steps of the first notebook, this notebook reads, cleans, pre-processes and prepares test datasets for prediction purposes.

SVM_Classification

In this notebook, the pre-processed training data is prepared for modelling. Hyper-parameter tuning is applied with Grid Search to find optimum hyper-parameters for the best performed model. Error analysis is conducted to further evaluate model performance. Finally, the pre-processed test sets are read and prepared for predictions. Results of predictions along with model performance macros are reported and saved.

BERT_Experiments

In this notebook various experiments have been run with the pre-trained BERT-base-uncased model. For each training loss curves have been reported. All experiments are logged and saved.

BERT_Predictions

The best performing BERT experiment have been used to classify texts from different test sets as offensive. Results of predictions along with model performance macros are reported and saved.