基于双向编码器表示模型和注意力机制的食品安全命名实体识别
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TP393

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国家重点研发计划项目“食品污染物风险分级评价框架体系与智能研判预警模型研究”(项目编号:2019YFC1606401)


Food safety named entity recognition based on Bert and attention mechanism
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Research on Food Contaminant Risk Classification Evaluation Framework System and Intelligent Research and Warning Model

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    摘要:

    针对于目前传统的命名实体识别模型在食品案件纠纷裁判文书领域的准确率不足的问题,在双向长短时记忆网络的基础上提出一种基于Bert和注意力机制的命名实体识别模型。模型通过Bert层进行字向量预训练,根据上下文语意生成字向量,字向量序列输入BiLSTM层和Attention层提取语义特征,再通过CRF层预测并输出字的最优标签序列,最终得到食品案件纠纷裁判文书中的实体。实验表明,该模型在食品纠纷法律文书上面的准确率和F1值分别达到了92.56%和90.25%,准确率相较于目前应用最多的BiLSTM-CRF模型提升了6.76%。Bert-BiLSTM-Attention-CRF模型通过对字向量的预训练,充分结合上下文语意,能够有效克服传统命名实体识别模型丢失字的多义性的问题,提高了食品案件纠纷裁判文书领域命名实体识别的准确率。

    Abstract:

    Aiming at the problem of insufficient accuracy of the current traditional named entity recognition model in the field of food dispute dispute judgment documents, a named entity recognition model based on Bert and attention mechanism is proposed based on the two-way long-term and short-term memory network. The model pre-trains the word vectors through the Bert layer, generates word vectors based on the contextual semantics, the word vector sequence is input to the BiLSTM layer and the Attention layer to extract semantic features, and then the CRF layer predicts and outputs the optimal label sequence of the word, and finally obtains a food case dispute judgment The entity in the instrument. Experiments show that the accuracy rate and F1 value of the model in the legal documents of food disputes have reached 92.56% and 90.25%, respectively, and the accuracy rate has been improved by 6.76% compared with the BiLSTM-CRF model that is currently most used. The Bert-BiLSTM-Attention-CRF model can effectively overcome the ambiguity of missing words in the traditional named entity recognition model through pre-training of word vectors and fully integrate the context semantics, which improves the recognition of named entities in the field of food dispute dispute judgment documents. Accuracy.

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姜同强,王岚熙. 基于双向编码器表示模型和注意力机制的食品安全命名实体识别[J]. 科学技术与工程, 2021, 21(3): 1103-1108.
Jiang Tongqiang, Wang Lanxi. Food safety named entity recognition based on Bert and attention mechanism[J]. Science Technology and Engineering,2021,21(3):1103-1108.

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  • 收稿日期:2020-05-15
  • 最后修改日期:2021-01-14
  • 录用日期:2020-07-04
  • 在线发布日期: 2021-02-09
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