An efficient named entity recognition for emergency plans

Tong Liu; Qiuyang Wang

doi:10.1117/12.2681637

1 June 2023 An efficient named entity recognition for emergency plans

Tong Liu, Qiuyang Wang

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 1271825 (2023) https://doi.org/10.1117/12.2681637
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

The context of emergency plan text is very tightly connected, while fully connected self-attention architecture Transformer has advantages in parallelism and remote context modeling, and Bi-directional Long-Short Term Memory (BiLSTM) can also capture long-distance context information. In this paper, we propose a new model Bi_ComL-CRF, the most important of which is the Com-LSTM unit, by introducing an additional Transformer encoded representation for each recurrent unit, then enhancing the captured context information through the new gating mechanism, so as to improve the effect of emergency plan NER task. We have carried out experiments on the emergency plan data, and the results show that our proposed model is better than other NER models, and can provide help for calculating the text similarity of emergency plans and generating emergency plans quickly.

Citation Download Citation

Tong Liu and Qiuyang Wang "An efficient named entity recognition for emergency plans", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 1271825 (1 June 2023); https://doi.org/10.1117/12.2681637

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