# Chinese Named Entity Recognition Based on Boundary Enhancement with Multi-Class Information
## 2 Related Studies  
In recent years, with the rapid development of neural network, neural models have been widely used for NER. The convolutional neural network (CNN) and recurrent neural network (RNN) are frequently used. External information embedding includes features such as radicals, dictionaries, word segmentation, and character shapes. Chinese named entity recognition involves a vast amount of external information, many of which are language-specific. The introduction of lexical information can provide abundant boundary information to the model and improve the accuracy of word segmentation. Char-Based model [9] and Word-Based model [10] are two common text representation models in natural language processing. They model and represent characters or words in different ways when processing text data. One considers each character in the text as the basic unit of modeling, and the other considers each word in the text as the basic unit of modeling to consider the overall semantic and syntactic structure of the word. The BiLSTM-CRF model [11] and IDCNN-CRF model [12] are based on the classical neural network structures (LSTM and CNN) and add a globally normalized CRF layer to improve the model's ability to model label sequences. [PERSON] et al. [13] proposed the Lattice LSTM model, laying the foundation for dynamic improvements in sequence modeling in Chinese NER. WC-LSTM [14] made slight improvements to Lattice LSTM by adding word information to the beginning or end characters of the word, effectively utilizing word boundary information and reducing the impact of Chinese word segmentation errors. LR-CNN [15] introduced a rethinking mechanism to merge words and used high-level features to guide lower-level weight distributions, which can better solve the problem of word boundary collisions. Experimental results showed that the model outperformed other dictionary-based models. [PERSON] et al. [16] proposed a soft lexicon approach, where the sentence is matched with a lexicon. For each character, SoftLexicon identifies all the words that contain it and assigns them to four categories. These four categories are then mapped to four vectors, which are concatenated with the character representation. The matching word information and word boundary information of each character are combined in the embedding layer of the model. [PERSON] et al. [17] dynamically stacked Flat NER layers to recognize entities and proposed a Dynamic Hierarchical Model. The model divides each nested named entity into multiple layers for recognition, and after each layer is completed, the obtained information is passed to the next layer of entity recognition. [PERSON] et al. [18] proposed an adversarial transfer learning framework and introduced a self-attention mechanism in the model to capture the global dependencies of the entire sentence. They incorporated the word boundary information shared in the Chinese Word Segmentation (CWS) task into the Chinese NER task. Chinese characters evolved from oracle bone script, and the early Chinese characters imitated the shapes of objects. The character shape information provides additional semantic information by incorporating pictographic elements, allowing for the extraction of rich pictorial information from the character's visual representation. [PERSON] et al. [19] first proposed the feasibility and practicality of radical embedding in Chinese processing. [PERSON] et al. [20] used Bidirectional Long Short-Term Memory (BiLSTM) to extract radical embeddings and then concatenated them with character embeddings containing multiple radicals to obtain the final character representation. With the improvement of the model, the use of a transformer in natural language processing can be more effective. MECT [21] used a two-stream transformer architecture, which allowed the model to learn and utilize two different types of features simultaneously: Chinese character features and root embedding.