Abstract:
OBJECTIVE Treatise on Cold Damage is one of the "Four Classics of Traditional Chinese Medicine, " containing a wealth of medical practice experience and medication rules. However, there has been insufficient data mining in the ancient literature of Treatise on Cold Damage, particularly due to the complex contextual semantics, making it challenging to fully grasp the interrelationships. This study aims to conduct entity recognition in Treatise on Cold Damage to facilitate comprehensive knowledge extraction.
METHODS A Bert-BiLSTM-RPRSA-CRF model was constructed based on the specialized terminology and concise sentence structure of the ancient literature. By incorporating a relative position representation self-attention (RPRSA) layer, this named entity recognition model aimed to identify entities within the text while learning information at different levels, thereby enhancing accuracy.
RESULTS Experimental verification demonstrated that our named entity recognition model achieved F1-Score, precision, and recall rates of 88.24%, 88.48%, and 88.00% respectively on the Treatise on Cold Damage dataset, outperforming other commonly used models.
CONCLUSION Our method outperforms other models in identifying entities within Treatise on Cold Damage, providing a foundation for information extraction from traditional Chinese medicine ancient texts such as Treatise on Cold Damage while offering effective means for intelligent assisted diagnosis and treatment in traditional Chinese medicine.
下载: