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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Sentence-level semantics are crucial for language understanding.
    • Existing relational triple extraction (RTE) models often overlook sentence semantics and inter-sample dependencies.
    • This leads to relation-unaware semantics and individually trained samples, hindering performance.

    Purpose of the Study:

    • To address limitations in current RTE models.
    • To incorporate relation information into text encoding for relation-aware semantics.
    • To leverage inter-sample dependencies for learning discriminative semantics.

    Main Methods:

    • Proposed a model-agnostic multi-relation detection task.
    • Developed model-agnostic multi-relation supervised contrastive learning.
    • Designed reverse label frequency weighting and hierarchical label embedding mechanisms.

    Main Results:

    • The proposed method significantly and consistently improved performance across various backbones.
    • Experiments were conducted on four public benchmarks.
    • Model analysis confirmed the effectiveness of the proposed techniques.

    Conclusions:

    • The novel approach enhances RTE models by integrating relation-aware semantics and inter-sample dependencies.
    • The method offers a flexible and effective way to improve relational triple extraction.
    • The techniques address challenges like label imbalance and relation hierarchy.