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    This study introduces a novel end-to-end double-graph method with relational enhancement (DGRE) for emotion-cause pair extraction (ECPE). DGRE improves upon two-step methods by modeling semantic and logical dependencies for more accurate emotion and cause identification.

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    Area of Science:

    • Natural Language Processing
    • Computational Linguistics
    • Artificial Intelligence

    Background:

    • Emotion-Cause Pair Extraction (ECPE) is crucial for understanding text.
    • Existing two-step methods for ECPE suffer from error propagation and neglect clause interactions.
    • A need exists for integrated approaches that capture semantic and logical dependencies.

    Purpose of the Study:

    • To propose an end-to-end Double-Graph method with Relational Enhancement (DGRE) for ECPE.
    • To model semantic and logical dependencies between emotions and their causes.
    • To improve the accuracy and robustness of ECPE.

    Main Methods:

    • Developed an end-to-end DGRE approach utilizing two graph encoders.
    • Employed Graph Attention Networks (GATs) for clause-level semantic embedding.
    • Integrated Relational Graph Convolutional Networks (RGCN) for pair-level refinement and introduced emotion-type classification for logical dependence.

    Main Results:

    • DGRE effectively models semantic and logical dependencies between clauses and pairs.
    • The approach establishes a communication mechanism between clauses and pairs from multiple perspectives.
    • Experiments on a Chinese corpus show DGRE outperforms state-of-the-art methods.

    Conclusions:

    • The proposed DGRE method offers a significant advancement in ECPE.
    • Modeling relational dependencies enhances the performance of emotion-cause extraction.
    • DGRE provides a more effective framework for understanding the relationship between emotions and their causes in text.