## 2 Related Works
### The NER Model  
#### 3.1.1 SoftLexicon Method  
SoftLexicon is proposed to solve the long-standing problem of the Chinese NER task. When performing an NER task, words in the context need to be labeled first. An English  
Figure 1: The overall architecture of the NER system. sentence can be segmented by space, while a Chinese sentence cannot be segmented directly due to the characteristics of the language. Therefore, researchers tends to use a CWS (Chinese Word Segment) tool for word segmentation first and then use a word-based sequence labeling model to label the segmented sentence [13]. In this process, due to the complexity of Chinese, it is impossible for the CWS tool to precisely segment each sentence. These segmentation errors will affect subsequent model training and entity prediction. To avoid these problems, some researchers conduct Chinese NER at the character level [39], but this will discard the latent word information contained in the sentence. Therefore, [PERSON] et al. (2018) integrated latent word information into a character-based NER model and proposed a Lattice-LSTM model, which can make full use of the information of word and word sequence without suffering segmentation errors. This model achieved SOTA results on some datasets at the time. However, the speed of model training was slowed down due to the insertion of additional word information into the input sequence. Moreover, the special structure of Lattice-LSTM is difficult to apply to other neural network models [11]. Therefore, [PERSON] et al. (2020) simplified the architecture of the model and proposed the SoftLexicon model. Compared to the Lattice-LSTM, the SoftLexicon method has faster inference speed and is easier to implement. The SoftLexicon method operates directly on the sequence representation layer of the NER model. For example, if the text is labeled with the \"BMES\" labeling method, each character \(c_{i}\) in a sentence that matches the lexicon will be classified into four word sets: \"B\" (Begin), \"M\" (Mediate), \"E\" (End) and \"S\" (Single). For words that cannot match the lexicon, the special symbol \"None\" will be filled in the word set (Figure 2). The process can be formulated as follows:  
\[ \begin{split} B(c_{i})&=\left\{w_{ik},\forall w_{ ik}\in L,i<k\leq n\right\}\\ M(c_{i})&=\left\{w_{ik},\forall w_{ik}\in L,1\leq j<i <k\leq n\right\}\\ E(c_{i})&=\left\{w_{ji},\forall w_{ji}\in L,1\leq j<i \right\}\\ S(c_{i})&=\left\{c_{i},\exists c_{i}\in L\right\} \end{split}  \tag{1}\]  
For a sentence \(s=\left\{c_{1},c_{2},\ldots\ldots,c_{n}\right\}\), the \(c_{i}\) in Formulas (1) represents each character in \(s\). The \(w_{i,k}\) represents the sub-sequence of \(s\) (the character set of \(c_{i}\) to \(c_{k}\)). \(L\) is the lexicon used in the experiment. After obtaining the word set of each character, each word set will be compressed into a fixed-dimension vector by using the pooling method, and the frequency of each word in the dataset will be used as the weight to adjust the result. \[v^{s}(B)=\frac{4}{Z}\sum_{w\in B}z(w)e^{w}(w) \tag{2}\]  
Here, \(v^{s}(B)\) is the vector representation of the word set \(B\), and \(z(w)\) denotes the occurrence frequency of the matched word \(w\) in the dataset. The symbol \(e^{w}(w)\) is the word  
Figure 2: An example of generating the “BMES” word set [13].