Abstract
The paper proposes a novel method called BPCNNER that enhances boundary information for Chinese nested named entity recognition using prompt learning. The method first involves populating the nested entities into a boundary prompt template based on predefined rules and combining it with the original text. The pre-trained BERT model is then used to obtain semantic features of the text, while the BiLSTM-CRF framework captures contextual information and calculates label dependencies. The experimental results demonstrate that the proposed method achieves an F1 score of 93.02% on the People's Daily dataset, outperforming other models. Therefore, the method is effective in improving Chinese nested named entity recognition, which is a challenging task due to the lack of effective boundary supervision.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (71503108, 62077029), the Research and Practice Innovation Project of Jiangsu Normal University (2022XKT1533).
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Li, Z., Song, M., Zhu, Y., Zhang, L. (2023). Chinese Nested Named Entity Recognition Based on Boundary Prompt. In: Yuan, L., Yang, S., Li, R., Kanoulas, E., Zhao, X. (eds) Web Information Systems and Applications. WISA 2023. Lecture Notes in Computer Science, vol 14094. Springer, Singapore. https://doi.org/10.1007/978-981-99-6222-8_28
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