## 3 Methodology
### Structured Data Classification Framework
#### 3.2.2 Keyword Augmentation Module  
In the design of the identification scheme, we observed that entity identification heavily relies on keywords. However, the lack of uniformity in data dictionaries across different departments and enterprises, as well as the reliance on manual labeling, presented challenges in achieving comprehensive keyword coverage. For instance, when referring to the income situation of an entity, keywords such as \"monthly salary\", \"salary,\" \"wage,\" \"income,\" \"treatment,\" and \"remuneration\" may be involved. Therefore, we proposed a keyword augmentation framework with a synonym discrimination model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Entity Type** & **Structured Recognition Method** \\ \hline Strong Rule Entities & Regular Expression \\ Weak Rule Entities & Keyword + Regular Expression \\ Irregular Entities & Keyword + Dictionary \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Entity recognition methods for structured data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entity Name** & **Sensitivity** & **Entity Type** & **Keywords** & **Features** \\ \hline Name & 3 & Irregular Entity & Name & Name \\ Gender & 3 & Weak Rule Entities & Gender & Gender (Broad) \\ Gender & 3 & Strong Rule Entities & - & Gender (Narrow) \\ Nationality & 3 & Irregular Entity & Nationality & Country Name \\ ID Effective Date & 3 & Weak Rule Entities & ID Effective Date & Date \\ Enrollment Date & 2 & Weak Rule Entities & Enrollment Date & Date \\ Personal Income & 3 & Weak Rule Entities & Personal Income & Amount \\ Deposit & 2 & Weak Rule Entities & Deposit & Amount \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Illustrative examples of UP-SDCG financial data hierarchical classification library. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Name** & **Regular Expression** & **Dictionary** \\ \hline Name & - & Chinese Name \\ Gender (Broad) & \(Male\|Female\|0\|1\|2\) & - \\ Gender (Narrow) & \(Male\|Female\) & - \\ Country Name & - & Country Name \\ Date & \(\backslash d\{4\}year(1[0-2][[1-9]]|0[1-9])day\) & \\  & \(\backslash d\{4\}(1[0-2][[1-9]|0[1-9])day\) & - \\ Amount & \(\wedge(-|\backslash+)?([1-9]\backslash d\{0,9\}|0)(\backslash\backslash d\{1,10 \})?\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Illustrative examples of UP-SDCG Features Library. * Keyword Augmentation Framework Keyword augmentation relies on the fundamental concept of synonym mining, which can be broadly categorized into two main types: knowledge-based augmentation and pattern-based augmentation. As illustrated in Figure 5, knowledge-based augmentation primarily relies on four types of knowledge bases:  
1. Publicly available financial datasets: We used publicly accessible financial structured data to accumulate keywords. For example, we extracted statistics provided by the China Banking and Insurance Regulatory Commission;
2. Extended Synonym Thesaurus: Considering the uniqueness of Chinese synonyms, we employed the Synonym Thesaurus [32] compiled by [PERSON] et al. [33] at the Harbin Institute of Technology Information Retrieval Laboratory as the foundation to construct the Extended Synonym Thesaurus. This extended version encompasses nearly 70,000 entries organized in a hierarchical tree-like structure, utilizing a five-level encoding pattern to classify the entries into five tiers: major category, intermediate category, minor category, word group, and atomic word group. Each atomic word group includes one or more synonymous words;
3. HowNet [34]: KnowNet utilizes tree-like sense-principal graphs and net-like sense-principal graphs to describe lexical properties;
4. Real data of the financial industry: We incorporated real information from the financial industry, specifically the banking industry interface. The central concept behind pattern-based keyword augmentation lies in bootstrapping. Bootstrapping is a statistical estimation method that involves inferring the distributional properties of the aggregate by resampling the observed information.