Effects of hyper-parameters setting in Bi-LSTM-CRF on Chinese named entity recognition

Taozheng Zhang; Pingping Ma

doi:10.1117/12.2680220

27 June 2023 Effects of hyper-parameters setting in Bi-LSTM-CRF on Chinese named entity recognition

Taozheng Zhang, Pingping Ma

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127053H (2023) https://doi.org/10.1117/12.2680220
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Named entity recognition is a basic task in the field of natural language processing. It also plays a role that can not be underestimated in the era of big data. This experiment will use the Bi-LSTM-CRF model to extract information from the input text to achieve the function of named entity recognition. In this experiment, we first select a suitable data set and perform vectorization, then build and train the Bi-LSTM-CRF model. At the same time, the dropout mechanism is added to assist. The optimal hyper-parameters will be found by constantly changing the parameter settings, so that the model shows the accuracy and robustness in the NER task. Each evaluation index reaches the optimal value. After the optimized model is obtained, the visualization of the model is carried out. All the entity parts are extracted from the input text and then output, showing the effect of the named entity recognition of the model and realizing a high level of named entity recognition.

Citation Download Citation

Taozheng Zhang and Pingping Ma "Effects of hyper-parameters setting in Bi-LSTM-CRF on Chinese named entity recognition", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127053H (27 June 2023); https://doi.org/10.1117/12.2680220

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