Qiuyu Lai1,2, Wang Kang3, Lei Yang1,2, Chun Yang1,2,*, Delin Zhang2,*
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 649-664, 2024, DOI:10.32604/iasc.2024.051724
Abstract Mathematical named entity recognition (MNER) is one of the fundamental tasks in the analysis of mathematical texts. To solve the existing problems of the current neural network that has local instability, fuzzy entity boundary, and long-distance dependence between entities in Chinese mathematical entity recognition task, we propose a series of optimization processing methods and constructed an Adversarial Training and Bidirectional long short-term memory-Selfattention Conditional random field (AT-BSAC) model. In our model, the mathematical text was vectorized by the word embedding technique, and small perturbations were added to the word vector to generate adversarial samples, while More >
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