A Chinese Nested Named Entity Recognition Model for Chicken Disease Based on Multiple Fine-Grained Feature Fusion and Efficient Global Pointer

文献类型： 外文期刊

第一作者： Wang, Xiajun

作者： Wang, Xiajun;Peng, Cheng;Li, Qifeng;Yu, Qinyang;Li, Pingping;Gao, Ronghua;Wu, Wenbiao;Jiang, Ruixiang;Yu, Ligen;Ding, Luyu;Zhu, Lei;Wang, Xiajun;Lin, Liqun;Li, Pingping;Peng, Cheng;Li, Qifeng;Yu, Qinyang;Gao, Ronghua;Wu, Wenbiao;Jiang, Ruixiang;Yu, Ligen;Ding, Luyu;Zhu, Lei;Peng, Cheng;Li, Qifeng;Yu, Qinyang;Gao, Ronghua;Wu, Wenbiao;Jiang, Ruixiang;Yu, Ligen;Ding, Luyu;Zhu, Lei

作者机构：

关键词： nested named entity recognition; chicken disease; multiple fine-grained feature fusion; RoBERTa; efficient global pointer

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2024 年 14 卷 18 期

页码：

收录情况： SCI

摘要： Featured Application This study proposes a multiple fine-grained nested named entity recognition model, which provides a solution for other specialized fields and lays the foundation for subsequent knowledge graph construction and intelligent inquiry system construction.Abstract Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species.

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