Deep adaptation of CNN in Chinese named entity recognition

Named entity recognition (NER) is an important task in the field of natural language processing, but it is more challenging in Chinese because of the lack of natural delimiters. The traditional character-based Chinese NER model directly uses long short-term memory (LSTM), gated recurrent units, and other sequence models to extract sentence-level information from character sequences, resulting in the lack of word-level information in the model. Therefore, a Chinese NER model called ChineseBERT-CNNs-BiLSTM-CRF was proposed, which uses the ChineseBERT pretrained model as the embedding layer so that the vector representation of each Chinese character contained pinyin, glyph, and conventional character information. In addition, a CNN-based neural network structure called CNNs was presented to extract word-level information from character sequences and alleviate the problem of entity boundary recognition. BiLSTM was used to extract global features (i.e., sentence-level information) and predict the corresponding labels of character sequences. Further, conditional random field (CRF) was employed to impose certain rules on the prediction of BiLSTM to enhance the recognition effect of the model. The experimental results revealed that the F1 values of the model on MSRA, people's Daily, and Weibo datasets reached 95.76, 96.61, and 70.00%, respectively, highlighting the effectiveness of the model.