# Research on Named Entity Recognition Based on Gated Interaction Mechanisms
## 1 Introduction  
Named entity recognition (NER) [1] has been a popular research field. NER technology has evolved from initially relying on rules and dictionaries to traditional machine learning models and then to various deep learning techniques [2]. Early methods based on rules and dictionaries combined linguistic features of text with relevant dictionaries, manually constructed rule templates suitable for such text, and finally used manual matching methods to identify entities related to the rule templates. These methods had disadvantages such as requiring a lot of manpower, poor portability, low reusability, and dependence on rule templates [3]. With the help of traditional machine learning models, researchers have treated named entity recognition as a sequence labeling task, with the most common being hidden Markov models [4], conditional random fields [5], and maximum entropy models [6]. The traditional hidden Markov model was improved by [PERSON] et al. by considering not only the features of the word itself but also the influence of the previous and next N event states, which improved the accuracy of the model's recognition [7]. Although traditional machine learning has to some extent alleviated the early over-reliance on template rules, improving model accuracy requires a large amount of labeled feature data. In recent years, named entity recognition tasks have seen a popular research direction shift towards methods based on deep learning. Traditional sequence labeling models have employed [8, 9] as the foundation for their named entity recognition tasks. [PERSON] andothers have addressed this by employing LSTM (long short-term memory) networks [10]. Subsequently, models such as [11; 12; 13; 14]. [PERSON] have combined convolutional neural networks with conditional random field models and attention mechanisms to construct a neural network framework for recognizing entities in Chinese texts [15]. Traditional convolutional neural networks and recurrent neural networks possess strong short-term memory effects, yet they are incapable of retaining feature information from distant sequences, resulting in gradient decay. References [16; 17] have utilized a model framework that amalgamates long short-term memory networks, bidirectional long short-term memory networks, and conditional random fields to mitigate gradient explosion and decay by acquiring bidirectional feature information from word vectors. This framework has been effectively applied to named entity recognition tasks with superior recognition capabilities. [PERSON] and others have integrated CNNs into the BiLSTM-CRF model framework to concurrently capture global context feature information and local key information, subsequently fusing feature information through a multi-head attention mechanism, successfully identifying entities in the domain of subway vehicle equipment and ameliorating the issue of insufficient features in this domain [18]. However, the aforementioned models rely on static word vectors generated by the Word2 Vec model as input, and when confronted with challenges such as lengthy named entities, the polysemy of single characters, and the synonymy of multiple words in Chinese, static word vectors fail to adequately represent the semantic information of words, thereby impacting the recognition performance of the models [19]. [PERSON] and others at Google proposed the pre-trained model BERT and subsequent improved pre-trained language models, which can capture character position information and bidirectional feature relationships from the corpus, dynamically generate context-related word vectors, effectively address the problem of polysemy in Chinese, and have been widely used in NER tasks [20]. Research [21] has introduced a bidirectional pre-trained language model, BERT, which dynamically acquires word vectors and then captures bidirectional contextual feature information.