@article{XU2024, 
author = {Cong XU and Huipeng SHI and Zhimin CHEN and Xinyu ZHANG and Jing WANG and Jiasen YANG},
title = {Satellite domain corpus construction and named entity recognition},
year = {2024},
journal = {Journal of National University of Defense Technology},
volume = {46},
number = {4},
pages = {175-183},
keywords = {name entity recognition, transfer learning, neural networks, data scarcity},
url = {https://www.sciopen.com/article/10.11887/j.cn.202404019},
doi = {10.11887/j.cn.202404019},
abstract = {Aiming at the lack of named entity corpus in the satellite domain and the low recognition performance of existing algorithms, a satellite domain entity labeling method considering fuzzy boundaries was proposed, constructed a corpus containing 8 common satellite domain entities where the granularity was finer and the coverage was wider in comparison with the existing corpora in this field. Based on this, a transfer learning and multi-network fusion satellite domain entity recognition algorithm was proposed. Algorithm used pretrained bidirectional encoder representations for transformers to smoothly transfer the semantics of the corpus for subword-level features, a BiLSTM (bi-directional long-short term memory) network for capturing contextual information to determine boundaries, and label prediction was achieved using a conditional random field as a decoder. Experimental results show that, compared with traditional models such as BiLSTM, the proposed algorithm has better recognition performance where the F1-score in 8 entities is all above 92% and the micro-average F1-score reaches 96.10%.}
}