Hate Speech Detection on Vietnamese Social Media Text using the Bidirectional-LSTM Model
Published in
arXiv.org(2019)External Links:
TL
TL;DR
Bidirectional Long Short-Term Memory is used to build the model that can predict labels for social media text according to Clean, Offensive, Hate.
Abstract
In this paper, we describe our system which participates in the shared task of Hate Speech Detection on Social Networks of VLSP 2019 evaluation campaign. We are provided with the pre-labeled dataset and an unlabeled dataset for social media comments or posts. Our mission is to pre-process and build machine learning models to classify comments/posts. In this report, we use Bidirectional Long Short-Term Memory to build the model that can predict labels for social media text according to Clean, Offensive, Hate. With this system, we achieve comparative results with 71.43% on the public standard test set of VLSP 2019.
References17 items
1
ABARUAH at SemEval-2019 Task 5 : Bi-directional LSTM for Hate Speech Detection
2
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection
3
Learning Word Vectors for 157 Languages
4
RNN on Machine Reading Comprehension Bi-Directional Attention Flow model
5