# Named Entity Recognition Method Based on Multi-Feature Fusion
## 2 Related Work  
Throughout the development process, the task of named entity recognition is mainly divided into three methods: rule-based and dictionary-based, statistical machine learning and deep learning. In the early named entity recognition task, most of the methods based on rules and dictionaries are used to deal with the entity extraction task. Scholars use linguistic knowledge to build rule templates and apply the created rule templates to the entity extraction task [5]. Although the method of named entity recognition based on a rule dictionary has high accuracy, the formulation of a rule template is highly dependent on the experience of the experts in the field of linguistics. Once other languages or multiple types of entities appear in the text, the formulation of rules becomes quite tedious and difficult, and the scope of the rule template is limited. If the unknown entity is beyond the scope, the problem of unrecognition will occur. Consequently, as computer technology advanced, statistical machine learning approaches replaced rule-based methods as the mainstream in NER. Techniques such as Support Vector Machines (SVM), Conditional Random Fields (CRF) and Hidden Markov Models (HMM) were widely employed in NER tasks. However, despite the interpretability and relatively high performance of statistical machine learning methods, the defects of over-reliance on feature engineering and high computational complexity are also reflected in the increasingly in-depth applications. In recent years, deep learning models, particularly those based on neural network architectures, have demonstrated the ability to automatically learn rich linguistic features from large volumes of textual data without relying on traditional, handcrafted features. This capacity for automatic feature extraction significantly simplifies model development and enhances the adaptability of models to new domains and datasets. Consequently, deep learning has gained substantial traction among researchers, with an increasing number using deep learning models to tackle NER as a sequence labeling task. Commonly used models include Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). [PERSON] et al. proposed a model based on Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa), specifically tailored for constructing a knowledge graph for air compressor fault diagnosis. By integrating Whole Word Masking (WWM) technology, Bidirectional Long Short-Term Memory (BiLSTM) and Conditional Random Fields (CRFs), their approach effectively extracts specific entities from unstructured data, establishing it as a classic architecture in NER [6]. Additionally, [PERSON] et al. combined BiLSTM with RoBERTa and Conditional Random Fields for information extraction in Chinese clinical texts. Experimental results indicated that this model was highly effective for medical entity recognition tasks and demonstrated unique potential in other medical NLP applications [7]. Furthermore, an increasing number of researchers are exploring novel methods to improve LSTM-based models. For instance, [PERSON] et al. applied an improved slime mold algorithm to optimize key hyperparameters of the BiLSTM network for NER, which enhanced the precision and robustness of the model [8]. [PERSON] et al. proposed a method that combines deep learning methods with word embedding technology and proposed a multi-convolution method based on attention-oriented BiLSTM to extract and analyze reviewers' opinions and emotional features. The results show that the model proposed in this article has significant improvements compared with other models [9]. In addition, [PERSON] et al. proposed a named entity recognition model based on ELECTRA.