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Progressive Multigranularity Information Propagation for Coupled Aspect-Opinion Extraction.

Fengmao Lv, Tao Liang, Zhihui Fei

    IEEE Transactions on Neural Networks and Learning Systems
    |October 28, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel network for coupled aspect-opinion extraction, improving how opinions are linked to aspects in text. The progressive multigranularity approach enhances information processing for more accurate sentiment analysis.

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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Computational Linguistics

    Background:

    • Coupled aspect-opinion extraction identifies (aspect, opinion) pairs or (aspect, opinion, sentiment) triplets from user-generated text.
    • Existing methods often overlook information propagation across different text granularities (words and word pairs).

    Purpose of the Study:

    • To propose a progressive multigranularity information propagation network for coupled aspect-opinion extraction.
    • To address the limitation of existing methods focusing only on atomic word-level interactions.

    Main Methods:

    • A progressive multigranularity information propagation network is introduced.
    • The model explores word-level correlations, updates word features using pairwise relation information, and propagates information among word pairs.
    • The task is treated as a unified relation prediction problem within an end-to-end framework.

    Main Results:

    • The proposed network effectively refines textual representations through iterative information propagation.
    • Comprehensive experiments on aspect-based sentiment analysis benchmarks demonstrate the approach's effectiveness.

    Conclusions:

    • The progressive multigranularity information propagation network significantly improves coupled aspect-opinion extraction.
    • This method offers a more robust way to handle complex opinion-related information in text analysis.