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Updated: Nov 9, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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Effective Collaborative Representation Learning for Multilabel Text Categorization.

Hao Wu, Shaowei Qin, Rencan Nie

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    Summary
    This summary is machine-generated.

    This study introduces a collaborative representation learning (CRL) model for multilabel text categorization (MLTC). CRL enhances document-label relationships for improved deep learning classification performance.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Natural Language Processing

    Background:

    • Deep learning models are prevalent for multilabel text categorization (MLTC).
    • Existing methods primarily focus on word-label relationships, neglecting document-label connections.
    • This gap limits the exploitation of global textual information.

    Purpose of the Study:

    • To propose an effective collaborative representation learning (CRL) model for MLTC.
    • To address the limitation of underexplored document-label relationships in existing models.
    • To improve the performance of deep learning-based text classification.

    Main Methods:

    • Developed a Collaborative Representation Learning (CRL) model.
    • Incorporated a factorization component for shallow document representations.
    • Integrated a neural component for deep text encoding and classification.
    • Employed alternating-least-squares for Pointwise Mutual Information (PMI) matrix factorization.
    • Utilized a multitask learning (MTL) strategy for neural component training.

    Main Results:

    • The CRL model explicitly leverages document-label relationships.
    • Experimental results on six datasets demonstrate competitive performance.
    • Achieved superior classification accuracy compared to state-of-the-art deep methods.

    Conclusions:

    • The proposed CRL model effectively captures document-label relationships.
    • CRL offers a promising approach for enhancing multilabel text categorization.
    • The joint training strategy optimizes both shallow and deep representations for better classification.