  In this paper, we present our participation in SemEval-2020 Task-12 Subtask-A
(English Language) which focuses on offensive language identification from
noisy labels. To this end, we developed a hybrid system with the BERT
classifier trained with tweets selected using Statistical Sampling Algorithm
(SA) and Post-Processed (PP) using an offensive wordlist. Our developed system
achieved 34 th position with Macro-averaged F1-score (Macro-F1) of 0.90913 over
both offensive and non-offensive classes. We further show comprehensive results
and error analysis to assist future research in offensive language
identification with noisy labels.
