tanyelai/lingda
UBMK 2022 Conference Paper: Linguistic-based Data Augmentation Approach for Offensive Language Detection
This project offers a new method for detecting offensive language in text. It takes raw text data and enhances it through linguistic-based augmentation to improve the accuracy of models that identify harmful content. This is useful for researchers and developers working on content moderation, online safety, or social media analysis.
No commits in the last 6 months.
Use this if you are developing or researching systems to automatically identify offensive language and need to improve your model's performance, especially when dealing with limited datasets.
Not ideal if you need a ready-to-use, production-grade content moderation API or tool without requiring deep technical implementation or research.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 21, 2024
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