Affiliations: [a] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China | [b] Shunde Innovation School, University of Science and Technology Beijing, Foshan, Guangdong, China | [c] Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China | [d] Ouyeel Co., Ltd., Shanghai, China
Correspondence:
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Corresponding author: Xiong Luo, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China. E-mail: [email protected].
Abstract: Named entity recognition (NER) as a crucial technology is widely used in many application scenarios, including information extraction, information retrieval, text summarization, and machine translation assisted in AI-based smart communication and networking systems. As people pay more and more attention to NER, it has gradually become an independent and important research field. Currently, most of the NER models need to manually adjust their hyper-parameters, which is not only time-consuming and laborious, but also easy to fall into a local optimal situation. To deal with such problem, this paper proposes a machine learning-guided model to achieve NER, where the hyper-parameters of model are automatically adjusted to improve the computational performance. Specifically, the proposed model is implemented by using bi-directional encoder representation from transformers (BERT) and conditional random field (CRF). Meanwhile, the collaborative computing paradigm is also fused in the model, while utilizing the particle swarm optimization (PSO) to automatically search for the best value of hyper-parameters in a collaborative way. The experimental results demonstrate the satisfactory performance of our proposed model.
Keywords: Named entity recognition (NER), particle swarm optimization (PSO), bi-directional encoder representation from transformers (BERT), collaborative optimization
