Chinese Medical Named Entity Recognition Based on Fusion of Global Features and Multi-Local Features
Huarong Sun, Jianfeng Wang, Bo Li, Xiyuan Cao, Junbin Zang, Chenyang Xue, Zhidong Zhang · IEEE Access · 2023
Chinese medical Named Entity Recognition (NER) is a task of Natural Language Processing (NLP), which aims to extract key information from Chinese medical texts. Recently, Transformer becomes the mainstream approach for NLP because of its powerful global feature extraction capability. However, entities usually appear in the form of subsequences in NER, therefore the local features are not negligible, and the uncertainty of Chinese word segmentation increases the difficulty of this task. In this paper, we propose a network structure that combines global feature extraction and multi-local feature extraction to enhance the performance of Chinese medical NER. Based on the global feature extraction by Transformer, we propose to use Bi-LSTM with a context integration mechanism to extract multi-local features to enhance the local semantic information of sequences, which integrates contextual information from the future and the past into each cell, and generates different weights through the gate mechanism to enhance the representational ability of each cell and thus the semantic information of the local sequences. And a feature fusion method based on attention mechanism is proposed, which allows the decoder to better focus on the more important information for predicting the current character. During the global feature extraction, the flat-lattice structure is introduced to generate all the potential results of Chinese word segmentation, and a span-based relative position coding is generated to capture the sequence characteristics. Finally, a CRF with conditional constraints is used as the decoder of the model. Experimental results on two benchmark datasets show the effectiveness of our model, and the method significantly outperforms the state-of-the-art methods in the medical NER task, achievingF1 value of 93.64% on CCKS2017 and 85.01% on CCKS2019.