School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Sensors (Basel). 2023 May 16;23(10):4812. doi: 10.3390/s23104812.
Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.
自然语言处理 (NLP) 技术作为一种重要的人工智能方法，在健康监测中发挥了关键作用。关系三元组提取作为 NLP 的一项关键技术，与健康监测的性能密切相关。本文提出了一种新的联合实体和关系提取模型，将条件层归一化与说话人头关注机制相结合，以增强实体识别和关系提取之间的交互作用。此外，所提出的模型利用位置信息来提高重叠三元组的提取准确性。在 Baidu2019 和 CHIP2020 数据集上的实验表明，所提出的模型可以有效地提取重叠三元组，与基线相比，性能有显著提高。
