融合Bert-Binary的垂直领域实体链接方法研究

J. Sensors Pub Date : 2022-08-23 DOI:10.1155/2022/4262270
Hairong Wang, Beijing Zhou, Bo Li, Xi Xu
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引用次数: 1

摘要

为解决中文文本实体边界不清晰、识别准确率低的问题,构建了农作物数据集,提出了一种基于bert二值的实体链接方法。候选实体集是通过多个数据源中的实体匹配生成的。调用Bert-binary模型计算候选实体的正确概率,筛选得分最高的实体进行链接。在作物数据集上与三种模型的对比试验中,最佳方法的f1值提高了2.5%,平均提高了8.8%。实验结果表明了本文提出的bert二值法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research of Vertical Domain Entity Linking Method Fusing Bert-Binary
To solve the problem of unclear entity boundaries and low recognition accuracy in Chinese text, we construct the crop dataset and propose a Bert-binary-based entity link method. Candidate entity sets are generated through entity matching in multiple data sources. The Bert-binary model is called to calculate the correct probability of the candidate entity, and the entity with the highest score is screened for linking. In comparative experiments with three models on the crop dataset, the F 1 value is increased by 2.5% on the best method or by 8.8% on average. The experimental results show the effectiveness of Bert-binary method in this paper.
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