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Interactive Lexical and Semantic Graphs for Semisupervised Relation Extraction.

Wanli Li, Tieyun Qian, Ming Zhong

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    Summary
    This summary is machine-generated.

    This study introduces a novel semisupervised relation extraction (RE) method that overcomes data limitations. It effectively leverages unlabeled data with minimal human input, significantly improving RE performance.

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    Area of Science:

    • Natural Language Processing
    • Machine Learning

    Background:

    • Relation extraction (RE) performance is limited by insufficient labeled data.
    • Existing semisupervised RE methods require manual rules or depend heavily on small labeled datasets.

    Purpose of the Study:

    • To present a novel semisupervised RE method that minimizes human effort and is robust to labeled data size.
    • To improve the augmentation of high-quality unlabeled samples for training.

    Main Methods:

    • Constructing lexical and semantic graphs using two simple rules to connect labeled and unlabeled samples.
    • Developing a graph interaction module to transfer knowledge and identify high-quality unlabeled samples.
    • Jointly recognizing samples with a classifier and the graph interaction module.

    Main Results:

    • The proposed method significantly outperforms state-of-the-art baselines on two public datasets.
    • Demonstrated effectiveness in leveraging unlabeled data with minimal human intervention.

    Conclusions:

    • The novel semisupervised RE method offers a robust and efficient solution to data scarcity.
    • The approach effectively utilizes lexical and semantic information for improved relation extraction.