Summary of English Offensive Text Detection Using Cnn Based Bi-gru Model, by Tonmoy Roy et al.
English offensive text detection using CNN based Bi-GRU model
by Tonmoy Roy, Md Robiul Islam, Asif Ahammad Miazee, Anika Antara, Al Amin, Sunjim Hossain
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel approach to detecting hate speech in social media by developing a Bi-GRU-CNN model that combines the strengths of bidirectional recurrent neural networks (Bi-GRUs) and convolutional neural networks (CNNs). The proposed model aims to classify text as offensive or not, leveraging the power of deep learning to identify abusive language and explicit images. Building upon existing research in this area, the authors demonstrate the effectiveness of their approach by comparing it with existing models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us deal with a big problem on social media – people sharing mean and hurtful things. There are many ways to share thoughts and ideas online, and some of these shares can be very negative. The internet is full of different platforms where we can post content, like Facebook and Twitter, but right now, there’s no easy way to keep this negative content from being shared. Researchers have been working on finding a solution to this problem by using computers to automatically sort through the content. This new study proposes a special type of computer model called Bi-GRU-CNN that can be used to identify whether text is offensive or not. |
Keywords
» Artificial intelligence » Cnn » Deep learning
