# Chinese Named Entity Recognition Based on Boundary Enhancement with Multi-Class Information
## 1 Introduction  
Named entity recognition (NER) is a fundamental information extraction task, which aims to identify named entities from predefined types of text, such as people's names, place names, and organization names [1]. Chinese is an important international language widely used all over the world. In recent years, Chinese NER has received increasing attention [2]. When dealing with Chinese text entity recognition, we enter text sentences into the model, train the model, and determine entity boundaries, i.e., the beginning and ending positions of a word, with the weights and other information of the sentences. The model selects the most optimal sequence of labels, then annotates the characters accordingly to accomplish the recognition of named entities. However, Chinese NER suffers tremendous challenges caused by the greater ambiguity of entity boundaries in Chinese text [2]. For example, a single Chinese character has less semantic information compared with an English word and often needs to be combined with others to express a specific meaning. In addition, Chinese text lacks distinctive features like space separation and capitalization of the first letter. Early NER methods can be divided into two types: rule-based methods and statistical-based methods [2]. The rule-based methods, which select matching entities from the text based on a number of matching rules, rely on linguistics experts to construct rules by hand. These methods are only suitable for use in specific domains and have less versatility. The statistical-based methods model NER as a sequence labeling problem, and as such are versatile and do not require too many hand-designed rules. In recent years, deep learning approaches to NER have received widespread attention in the field. The deep learning-based methods do not need to manually formulate rules or cumbersome features and caneasily extract implicit semantic information from the input, making them flexible and easy to migrate to new fields. In recent years, most research efforts have focused on incorporating external information into character representations to enhance semantic information and improve boundary recognition in Chinese NER [3; 4; 5; 6]. However, the types of information introduced in most studies are limited, and little attention is given to the structure of text sentences and contextual information. Introducing embedding information can improve the recognition performance to some extent, but it is limited by the constraints of single types of information such as one-word polyphone or glyphs information. Although these kinds of lexical words can add semantic information, the extras are context-independent [7]. Comparably, part-of-speech (POS) tags, syntactic constituents, and dependency relations are crucial for analyzing sentence structure in a sentence. On the one hand, entities are typically nouns, and POS tags are helpful in determining whether a word is an entity. On the other hand, the POS of a character is influenced by the POS of its surrounding characters, which aids in identifying the positions of entities with similar context. Syntactic constituents and dependency relations can identify the structure within a text and guide the model in locating corresponding entities. Because the boundary demarcation may affect the correctness of subsequent sequence labeling, it is necessary to integrate various types of information to provide different semantic cues. Furthermore, it is important to analyze the structural aspects of the text and consider the context to enhance the boundaries. In this paper, we propose a boundary enhancement with multi-class information model (BEMCI) in order to tackle the issues raised in previous studies [8] by utilizing boundary enhancement technology with multi-class information fusion.