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Semantic-consistent learning for one-shot joint entity and relation extraction

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Abstract

Entity and relation extraction is an essential task in knowledge acquisition and representation. Since novel relations continue to emerge in practice, the extraction for novel relational triplets is urgently needed but understudied. Existing works heavily rely on large-scale artificial external knowledge instead of just the corpus itself to predict novel relations, which require costly human work. In this paper, we propose a semantic-consistent learning method (SCL) for one-shot joint entity and relation extraction, whichbootstraps novel relational triplets from only a single instance of each class. The key is to take full advantage of semantic consistency. Specifically, we estimate the semantic consistency not only between relations and triplets by entropy to refine candidate triplets generated by a joint model, but also between triplets and plain texts by template-based contrastive learning for candidate triplets ranking. Experimental results on benchmarks demonstrate that our method can generalize novel relational triplets with strong robustness and achieve promising performance by micro F1-score 45.12% and 67.02% on two common datasets, respectively.

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Data Availability

The datasets analyzed during the current study are available from [3] and [15]. Data are available from the authors upon reasonable request and permission.

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Authors and Affiliations

  1. College of Artificial Intelligence, Beijing University of Posts and Telecommunications, Haidian District, 10 Xitucheng Road, Beijing, 100876, Beijing, China

    Jinglei Li, Yajing Xu, Hongzhan Lin, Guang Chen & Bosen Zhang

  2. The National Computer Network Emergency Response Technical Team, Coordination Center of China, Haidian District, Beijing Zhongguancun South Street on the 4th, Beijing, 100190, Beijing, China

    Boya Ren

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  1. Jinglei Li
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  2. Yajing Xu
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  3. Hongzhan Lin
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  4. Guang Chen
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  5. Bosen Zhang
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  6. Boya Ren
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Correspondence to Yajing Xu.

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Li, J., Xu, Y., Lin, H. et al. Semantic-consistent learning for one-shot joint entity and relation extraction. Appl Intell 53, 5963–5976 (2023). https://doi.org/10.1007/s10489-022-03812-w

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