Abstract: In order to solve the problem of poor named entity recognition due to the complex entity and the nesting of entities in the field of exotic marine organisms, the convolutional neural network (CNN)-bidirectional gated recurrent unit network (BiGRU)-conditional random field (CRF) network were used to identify the exotic marine biological entities, and the word vector, part of speech feature vector and other features as the joint input of the network were constructed to improve the recognition effect of the network. Results showed that there was 90.62% of average accuracy of the named entity recognition on the three types of exotic marine biological entities, time entities, and place name entities, the average recall rate of 89.50%, and the average F1 value of 90.05%, which is greatly improved compared to traditional entity recognition methods, using the CNN-BiGRU-CRF network fused with multiple feature vectors. It was found that the network proposed in this study fully extracted and utilized text features, and solved the problem of long-distance dependence of text, with better recognition effect for named entity recognition in the field of exotic marine organisms.
贺琳, 张雨, 巴韩飞. 基于注意力机制和深度学习模型的外来海洋生物命名实体识别[J]. 大连海洋大学学报, 2021, 36(3): 503-509.
HE Lin, ZHANG Yu, BA Hanfei. Named entity recognition of exotic marine organisms based on attention mechanism and deep learning network. Journal of Dalian Ocean University, 2021, 36(3): 503-509.
2021, Vol. 36 
