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MGEL: Multigrained Representation Analysis and Ensemble Learning for Text Moderation.

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    This study enhances text moderation by developing a multihot byte-level scheme for character representation and a weighting approach for logistic regression. It also improves BERT for handling word obfuscations, advancing abusive language detection.

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    Area of Science:

    • Natural Language Processing
    • Computational Linguistics
    • Machine Learning for Content Moderation

    Background:

    • Text classification methods face challenges in text moderation, particularly with multibyte characters and intentional word obfuscations.
    • Existing models struggle to accurately represent diverse character sets and detect deliberately altered abusive language.

    Purpose of the Study:

    • To address limitations in text classification for content moderation.
    • To improve the representation of multibyte characters and enhance the detection of obfuscated words.
    • To advance the state-of-the-art in abusive language detection.

    Main Methods:

    • Developed a multihot byte-level scheme to reduce character encoding dimensions.
    • Introduced a weighting approach for fusing n-gram features to enhance logistic regression.
    • Created an enhanced BERT variant integrating byte and character decomposition to handle word obfuscations.

    Main Results:

    • The enhanced logistic regression model outperformed well-tuned neural networks.
    • The improved BERT variant significantly advanced state-of-the-art performance on large abusive language datasets.
    • The proposed methods effectively tackle challenges posed by word obfuscations in text moderation.

    Conclusions:

    • The developed framework offers a feasible and effective solution for text moderation challenges.
    • Byte-level representations and enhanced feature fusion improve classification accuracy.
    • Integrating byte and character decomposition is crucial for robustly detecting obfuscated abusive language.