Entity relation extraction based on pretrained model and multi-fork decoding tree

Xin Zhang; Guangming Xian; Cenyu Zhou; Haoyang Mei

doi:10.1117/12.2655930

12 January 2023 Entity relation extraction based on pretrained model and multi-fork decoding tree

Xin Zhang, Guangming Xian, Cenyu Zhou, Haoyang Mei

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125090U (2023) https://doi.org/10.1117/12.2655930
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

Aiming at the problems that the previous entity relation extraction model has insufficient dependencies between words, the recognition effect of overlapping entities is low, and the triples caused by single decoding force an unnecessary order, this paper proposes a deep learning-based method. The method uses a pre-trained model to extract sentence features, uses the Span method with stronger ability to extract overlapping entities for entity extraction, and uses a deep multi-fork decoding tree to implement parallel decoding. The experimental results on the CoNLL04 and ADE datasets show that compared with other relation extraction models, the F1 value of the model in this paper has a better improvement, and it also verifies the effectiveness and generalization ability of the model in this paper.

Citation Download Citation

Xin Zhang, Guangming Xian, Cenyu Zhou, and Haoyang Mei "Entity relation extraction based on pretrained model and multi-fork decoding tree", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125090U (12 January 2023); https://doi.org/10.1117/12.2655930

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