航空航天
计算机科学
命名实体识别
人工智能
变压器
领域(数学分析)
特征(语言学)
人工神经网络
领域知识
模式识别(心理学)
自然语言处理
机器学习
工程类
数学分析
语言学
哲学
电气工程
数学
系统工程
电压
任务(项目管理)
航空航天工程
作者
Jing Chu,Yumeng Liu,Qi Yue,Zixuan Zheng,Xiaokai Han
标识
DOI:10.1038/s41598-023-50705-0
摘要
Abstract In recent years, along with the rapid development in the domain of artificial intelligence and aerospace, aerospace combined with artificial intelligence is the future trend. As an important basic tool for Natural Language Processing, Named Entity Recognition technology can help obtain key relevant knowledge from a large number of aerospace data. In this paper, we produced an aerospace domain entity recognition dataset containing 30 k sentences in Chinese and developed a named entity recognition model that is Multi-Feature Fusion Transformer (MFT), which combines features such as words and radicals to enhance the semantic information of the sentences. In our model, the double Feed-forward Neural Network is exploited as well to ensure MFT better performance. We use our aerospace dataset to train MFT. The experimental results show that MFT has great entity recognition performance, and the F 1 score on aerospace dataset is 86.10%.
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