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Updated: Jun 27, 2025

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HRCL: Hierarchical Relation Contrastive Learning for Low-Resource Relation Extraction.

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    IEEE Transactions on Neural Networks and Learning Systems
    |April 29, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Hierarchical Relation Contrastive Learning (HRCL) improves low-resource relation extraction by using prompts and hierarchical clustering to overcome data limitations. This novel approach significantly outperforms existing methods in relation extraction tasks.

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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Low-resource relation extraction (LRE) faces challenges due to limited annotated data.
    • Existing methods like self-training and instance-wise contrastive learning have limitations, including error accumulation and difficulty distinguishing similar semantics.

    Purpose of the Study:

    • To introduce a novel contrastive learning framework, Hierarchical Relation Contrastive Learning (HRCL), to address the challenges in LRE.
    • To enhance the efficacy of contrastive learning by generating high-level relation representations and optimizing pair-level relation features.

    Main Methods:

    • HRCL utilizes task-related instruction descriptions and schema constraints as prompts.
    • Hierarchical Affinity Propagation Clustering (HiPC) derives hierarchical signals from relational feature space.
    • A Hierarchy Cross-Attention (HCA) mechanism is employed for relation-wise contrastive learning.

    Main Results:

    • HRCL demonstrates effectiveness and robustness across five public relation extraction datasets in low-resource settings.
    • The proposed HRCL model outperforms the current state-of-the-art (SOTA) by an average of 6.56% in B3F1 score.
    • Experimental results validate the superiority of HRCL in low-resource relation extraction scenarios.

    Conclusions:

    • HRCL offers a significant advancement in tackling the complexities of low-resource relation extraction.
    • The framework effectively overcomes the limitations of previous approaches by leveraging hierarchical structures and contrastive learning.
    • The publicly available source code facilitates further research and application of HRCL.