## 2 Related Work  
NER is an important step in building a knowledge graph and is the basic work of information extraction, which lays the foundation for entity alignment, relationship extraction, etc. [5]. NER methods are divided into three main categories: rule dictionary [6]-based methods, machine learning-based [7] methods, and deep learning-based methods. Most of the early NER used rule-based approaches, mainly by manually constructing limited rules to find strings in the text that match the rules. The rule-based approach has high accuracy in specific domains and is suitable for domains with small data sets that do not require frequent updates, but the approach requires a more comprehensive set of rules to be constructed manually, which has the problems of time-consuming manual operation and poor portability. With the rise of machine learning, the NER methods started to rely on various statistical models, marking a small number of samples by manually setting features and then training the model. Therefore, this method has good portability. It mainly includes Hidden Markov Model (HMM) [8], CRF [9], and Support Vector Machine (SVM) [10] for entity recognition. HMM is an indirect observation of the state of Markov chains through a sequence of observation vectors, which generates the corresponding observation vectors from each probability density distribution of the state sequence and contains two basic assumptions. Since the model uses the Viterbi algorithm, the training speed is relatively fast. However, its output relies on the independence assumption, which leads to the inability to consider long-range dependence and cannot truly describe the information of the office letter in the sequence, which limits the selection of features to a certain extent and can only achieve local optimality and is more suitable for short textual entity recognition. The principle of SVM is to map the input vector x into a high-dimensional feature space by means of a pre-selected nonlinear mapping and construct the optimal classification hyperplane to separate the two classes of samples without errors and to maximize the classification gap between the two classes. Although SVM can handle high-dimensional features, the training time is relatively long. CRF is a conditional probability distribution model for another set of output sequences given a set of input sequence conditions, with no strict independence assumption conditions; it can accommodate arbitrary contextual information and has a flexible feature design. Among the traditional machine learning methods, CRF is seen as the dominant model for NER, with the advantages that in the process of labeling each location, CRF can output the global optimal sequence using internal and contextual feature information, and its high accuracy in predicting entity types makes it a commonly used machine learning-based method for NER. [PERSON] et al. [11] proposed to use HMM for initial entity recognition using lexical properties as observations for the semantic incompleteness feature of short texts and to construct a phonetic cognate relationship database to identify potential entities based on the recognition results. For example, [PERSON] et al. [12] used the SVM approach for Vietnamese news text and selected words and lexicality as features for entity recognition at syntactic and semantic levels, respectively, to build a Vietnamese news text classification model. [PERSON] et al. [13] proposed a method based on a combination of HMM and SVM for place name recognition. Firstly, the labeled place name training set is used to train HMM for place name recognition, and then the recognized place names are matched with GeoNames' place names list, and the informative and relevance features of the place names are calculated and input to SVM to get the final place name recognition results. [PERSON] et al. [14] implemented NER in TCM electronic medical records based on the CRF model, which introduced contextual indicator words, lexicon, and other features and achieved 80% F1 value for symptoms.