| Autor: |
Lai, Qiuyu, Kang, Wang, Yang, Lei, Yang, Chun, Zhang, Delin |
| Předmět: |
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| Zdroj: |
Intelligent Automation & Soft Computing; 2024, Vol. 39 Issue 4, p649-664, 16p |
| Abstrakt: |
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 local features were extracted by Bi-directional Long Short-Term Memory (BiLSTM). The self-attentive mechanism was incorporated to extract more dependent features between entities. The experimental results demonstrated that the AT-BSAC model achieved a precision (P) of 93.88%, a recall (R) of 93.84%, and an F1-score of 93.74%, respectively, which is 8.73% higher than the F1-score of the previous Bi-directional Long Short-Term Memory Conditional Random Field (BiLSTM-CRF) model. The effectiveness of the proposed model in mathematical named entity recognition. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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