# Research on Named Entity Recognition Based on Gated Interaction Mechanisms
## 1 Introduction
This approach, in conjunction with BiLSTM-CRF decoding, has improved the accuracy by 10.06% for identifying entities within specific domains, thereby alleviating the problem of low accuracy in text entity recognition in those domains. [PERSON] et al. have utilized the BERT pre-trained model to obtain word vectors containing position information, extracted global and local features using BiLSTM and ID-CNN, respectively, and connected them. They have identified COVID-19 epidemiological entities through conditional random field decoding, solving the problem of insufficient local feature extraction and achieving an accuracy of 94.8% [22]. [PERSON] et al. have proposed a BERT-BiLSTM-CRF text model framework and introduced adversarial training. The framework has treated named entity recognition as the main task and Chinese word segmentation as the auxiliary task, sharing word vector boundary information to help the model recognize entity boundaries, and has improved the accuracy by 3.25% compared to the baseline model [23]. Addressing the lack of Chinese character shape and pronunciation features in Chinese pre-trained models, [PERSON] et al. have integrated the visual features of character shape and pronunciation into the pre-training phase of the model to enhance its understanding of Chinese grammar and semantics [24]. In the process of constructing knowledge graphs, accurate entity recognition is a necessary technique. The gated recurrent unit (GRU) has been favored due to its relatively simple structure and fewer parameters, making it easier to converge during training, especially when the training set is small. As a result, the GRU has been increasingly applied to entity recognition tasks [25]. However, there are still issues in Chinese named entity recognition, such as blurry entity boundary information, potential semantic conflicts between words, and the inability to further utilize potential semantic information. Therefore, further research is needed to understand how to combine the Mogrifier [26] mechanism with named entity recognition to deeply mine the implicit semantic information in the text and establish a more accurate named entity recognition model. In response to the above issues, this paper proposes a named entity recognition model based on a gating interaction mechanism. First, the dynamic vector representation of the text is obtained through the BERT pre-trained language model. Then, the Mogrifier BiGRU model is used to enhance the interaction between context information while extracting features from the input vectors. Additionally, an adaptive optimization algorithm is introduced for the gating interaction unit to automatically adjust and optimize its parameters. Finally, a state transition matrix is used to improve the reliability of the prediction results. The main contributions of the research can be summarized as follows:  
1. By introducing the BERT pre-trained model and adding a gating interaction mechanism, we enhance local feature information. The dynamic vector representation and feature enhancement effectively solve the problem of polysemy in Chinese. 2. Based on the named entity recognition model, we have improved the research on the gating interaction mechanism. By setting more hyperparameters to capture the implicit semantic change rules and using Bayesian optimization algorithms to select hyperparameters in the model, we have achieved automatic calculation of the model's hyperparameters, thereby improving the accuracy of the named entity recognition model.