Named entity recognition (NER) is a subtask in natural language processing, and its accuracy greatly affects the effectiveness of downstream tasks. Aiming at the problem of insufficient expression of potential Chinese features in named entity recognition tasks, this paper proposes a multifeature adaptive fusion Chinese named entity recognition (MAF-CNER) model. The model uses bidirectional long short-term memory (BiLSTM) neural network to extract stroke and radical features and adopts a weighted concatenation method to fuse two sets of features adaptively. This method can better integrate the two sets of features, thereby improving the model entity recognition ability. In order to fully test the entity recognition performance of this model, we compared the basic model and other mainstream models on Microsoft Research Asia (MSRA) and “China People’s Daily” dataset from January to June 1998. Experimental results show that this model is better than other models, with F1 values of 97.01% and 96.78%, respectively.
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机译：命名实体识别（ner）是自然语言处理的子任务，其精度极大地影响了下游任务的有效性。针对潜在的中国特色表达不足的问题，该论文提出了一种多地区自适应融合中文命名实体识别（MAF-CNER）模型。该模型使用双向长期内存（BILSTM）神经网络来提取笔划和激进特征，采用加权级联方法，自适应地保险两组特征。该方法可以更好地集成两组特征，从而提高了模型实体识别能力。为了完全测试本型号的实体识别性能，我们将基本模式和其他主流模型与Microsoft Research Asia（MSRA）和“中国人民日报”数据集进行了比较了1998年至6月。实验结果表明，该模型更好而不是其他模型，F1值分别为97.01％和96.78％。
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