IIITG-ADBU at HASOC 2019: Automated hate speech and offensive content detection in english and code-mixed Hindi text

Abstract

This paper presents the results obtained by using Logistic Regression (LR), Support Vector Machine (SVM), bi-directional long short-term memory (BiLSTM) and Neural Network (NN) models for subtask A of the shared task \Hate Speech and Offensive Content Iden- tification in Indo-European Languages' (HASOC). This paper presents the results for English and code-mixed Hindi language. Embeddings from Language Models (ELMo), Glove and fastText embeddings, and TF-IDF features of character and word n-grams have been used to train the models. Our best models for Hindi and English language obtained F1 score of 81.05 and 74.62 respectively on the official run. The models obtained the 4th and 8th position in the official ranking.