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- W4313367663 abstract "Social media has effectively replaced traditional forms of communication and marketing. As these platforms allow for the free expression of ideas and facts through text, images, and videos, there exists a significant need to screen them to safeguard people and organisations from objectionable information directed at them. Our work aims to categorise code-mixed social media comments and posts in Tamil, Malayalam, and Kannada into offensive or not offensive at different levels. We present a multilingual MPNet and CNN fusion model for detecting offensive language content directed at an individual (or group) in low-resource Dravidian languages at different levels. Our model is capable of handling data that has been code-mixed, such as Tamil and Latin scripts. The model was successfully validated on the datasets, achieving offensive language detection results better than those of other baseline models with weighted average F1-score of 0.85, 0.98, and 0.76, and performed better than the baseline models EWDT, and EWODT by 0.02, 0.02, 0.04 for Tamil, Malayalam, and Kannada respectively." @default.
- W4313367663 created "2023-01-06" @default.
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- W4313367663 date "2023-04-01" @default.
- W4313367663 modified "2023-10-14" @default.
- W4313367663 title "Offensive language identification in dravidian languages using MPNet and CNN" @default.
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- W4313367663 doi "https://doi.org/10.1016/j.jjimei.2022.100151" @default.
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