Chinese Named Entity Recognition Based on BERT and Lightweight Feature Extraction Model

In the early named entity recognition models, most text processing focused only on the representation of individual words and character vectors, and paid little attention to the semantic relationships between the preceding and following text in an utterance, which led to the inability to handle the problem of multiple meanings of a word during recognition. To address this problem, most models introduce the attention mechanism of Transformer model to solve the problem of multiple meanings of a word in text. However, the traditional Transformer model leads to a high computational overhead due to its fully connected structure. Therefore, this paper proposes a new model, the BERT-Star-Transformer-CNN-BiLSTM-CRF model, to solve the problem of the computational efficiency of the traditional Transformer. First, the input text is dynamically generated into a character vector using the BERT model pre-trained in large-scale preconditioning to solve the problem of multiple meanings of words, and then the lightweight Star-Transformer model is used as the feature extraction module to perform local feature extraction on the word vector sequence, while the CNN-BiLSTM joint model is used to perform global feature extraction on the context in the text. The obtained feature sequences are fused. Finally, the fused feature vector sequences are input to CRF for prediction of the final results. After the experiments, it is shown that the model has a significant improvement in precision, recall and F1 value compared with the traditional model, and the computational efficiency is improved by nearly 40%