Abstract
The complexity of syntax and the specialized nature of Chinese electronic medical record data make it challenging to accurately identify medical entities using named entity recognition models. In order to precisely extract complex medical vocabulary from electronic medical records, this paper proposes a multi-feature-based named entity recognition model that addresses the issue of insufficient internal feature extraction in the FLAT model. Firstly, the radical and pinyin features of Chinese characters are extracted for enrichment and perfection of their semantic information. These features are then combined with the word embeddings extracted by the FLAT-lattice method. Finally, the fused features and position encoding are input to the Transformer encoder for encoding, followed by decoding using the CRF method. Experimental results demonstrate that the proposed model outperforms many existing algorithms on the CCL2021 dataset, with an F1 score of up to 91.75%.
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