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Related Experiment Video

Updated: Nov 8, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Weakly Supervised Domain Adaptation for Aspect Extraction via Multilevel Interaction Transfer.

Tao Liang, Wenya Wang, Fengmao Lv

    IEEE Transactions on Neural Networks and Learning Systems
    |April 19, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for aspect term extraction, using sentence-level labels to improve cross-domain performance. The approach leverages multilevel reconstruction for better knowledge transfer in opinion analysis.

    Related Experiment Videos

    Last Updated: Nov 8, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    820

    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence

    Background:

    • Fine-grained aspect term extraction is crucial for aspect-based opinion analysis, identifying product/service opinion targets.
    • Current methods require expensive token-level annotations, limiting their real-world applicability.
    • Existing domain adaptation strategies struggle with fine-grained prediction and large domain gaps.

    Purpose of the Study:

    • To investigate the use of readily available sentence-level aspect category labels for improving token-level aspect term extraction.
    • To develop a novel approach for cross-domain aspect term extraction that overcomes limitations of previous methods.
    • To demonstrate the effectiveness of leveraging sentence-level information for knowledge transfer.

    Main Methods:

    • Proposing a novel multilevel reconstruction mechanism to align fine- and coarse-grained information.
    • Utilizing sentence-level aspect category labels as pivot knowledge for cross-domain transfer.
    • Assuming invariance of interactions between sentence-level categories and token-level terms across domains.

    Main Results:

    • The proposed approach effectively utilizes sentence-level aspect category labels.
    • Significant performance gains were achieved in cross-domain aspect term extraction.
    • Comprehensive experiments on benchmark datasets validate the method's efficacy.

    Conclusions:

    • Leveraging sentence-level aspect category labels is a viable and effective strategy for improving cross-domain aspect term extraction.
    • The multilevel reconstruction mechanism successfully bridges the gap between sentence-level and token-level information.
    • This work offers a practical solution for aspect term extraction in domains with limited annotations.