Enterprise AI Impact Analysis
A Study on Named Entity Recognition in Classical Chinese Medical Texts Using BERT-BIGRU-CRF
This analysis explores the innovative application of a hybrid BERT-BiGRU-CRF architecture for named entity recognition in classical Chinese medical texts, demonstrating significant advancements in semantic understanding and entity identification.
Executive Impact Summary
The proposed BERT-BiGRU-CRF model significantly enhances named entity recognition in complex classical Chinese medical texts, offering precise identification of critical entities for knowledge graph construction and intelligent systems.
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Deep Analysis & Enterprise Applications
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The BERT-BiGRU-CRF Architecture
The BERT-BiGRU-CRF named entity recognition model employs a three-tier architecture comprising 'pre-trained semantic representations, sequence feature extraction, and global sequence optimisation' to effectively address the complex semantic structures and polysemy inherent in classical Chinese medical texts. The model first encodes input text within the BERT pre-training framework to generate high-quality word vectors, resolving lexical ambiguity in traditional Chinese medical terminology. Subsequently, a BiGRU model captures contextual and long-range semantic features implicit within classical texts. Finally, a CRF model decodes predicted entity labels to output optimal sequence tags.
Performance Across Entity Categories
The model demonstrates varied performance across different entity categories, highlighting its nuanced understanding and identification capabilities.
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Enterprise Process Flow
BERT Encoding Layer
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BiGRU Feature Extraction
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CRF Decoding Layer
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Optimal Sequence Tags
Model Performance Comparison
The BERT-BiGRU-CRF model demonstrates superior performance compared to traditional baseline models, validating its effectiveness in named entity recognition for classical Chinese medical texts.
| Model | Precision (P) | Recall (R) | F1 (%) |
|---|---|---|---|
| BERT-BILSTM-CRF | 86.09 | 88.29 | 87.18 |
| BERT-BIGRU-CRF | 88.09 | 90.93 | 89.49 |
| BIGRU-CRF | 85.53 | 85.77 | 85.65 |
| BILSTM-CRF | 85.82 | 85.04 | 85.43 |
| BERT-CRF | 87.03 | 90.42 | 88.69 |
4.06%
F1 Score Improvement over BiLSTM-CRF
The BERT-BiGRU-CRF model achieves a 4.06% higher F1 score compared to the BiGRU-CRF baseline model, underscoring the formidable performance of pre-trained language models in deep semantic mining.
Future Research and Implementation Roadmap
Considering the current state of research, future work will focus on two directions: first, expanding the dataset's scale and coverage by incorporating classical TCM texts from different dynasties and academic schools, with particular emphasis on supplementing rare entity samples and borderline cases to further enhance the model's generalisation capability and robustness; second, optimising the model architecture design to achieve further improvements in evaluation metrics.
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