## 3 Data and Methodology  
### Data  
In this study, we used text from the Zhengzhou Epidemiological Survey Data (COVID-19 ESD) released on social media. A total of 77,451 pieces of data were available. The dataset was divided into training and test sets at a ratio of 4:1. To label these data by referring to the Chinese national rules for the geocode of addresses and the standard specification on industrial address classification and considering the universality, uniformity, and extensibility of the address, this study used BMES labeling [42] to categorize the Chinese address elements into the provincial, municipal, county (district), and town (township) administrative divisions, villages, roads, local regions, doorplates, building addresses, and unit numbers. The specific classifications are listed in Table 1.  
Moreover, to validate the model's ability to generalize, this study introduced two distinct datasets, namely People's Daily Annotated Corpus (PFR) and Microsoft Research Asia (MSRA), in addition to the COVID-19 ESD dataset created for this research. These datasets differ in that the PFR and MSRA datasets consist of a sole entity type that is labeled as \"LOC\", referring to geographical placenames. The partitioning of training and testing sets for both the PFR and MSRA datasets adhered to a 4:1 ratio. The PFR dataset, extracted from the January 1998 edition of the People's Daily newspaper's annotated corpus ([[https://www.heywhale.com/mw/dataset/5](https://www.heywhale.com/mw/dataset/5) ce7983 cd10470002b334 de3/content]([https://www.heywhale.com/mw/dataset/5](https://www.heywhale.com/mw/dataset/5) ce7983 cd10470002b334 de3/content) (accessed on 15 January 2023)), encompasses over six million bytes of text. It has found extensive use in international competitions focused on named entity recognition tasks for geographical places and personal names. Owing to its proven reliability and gradual adoption, it has evolved into the most widely utilized standard corpus in this domain. The MSRA corpus [43] ([[https://tianchi.aliyun.com/dataset/144307](https://tianchi.aliyun.com/dataset/144307)]([https://tianchi.aliyun.com/dataset/144307](https://tianchi.aliyun.com/dataset/144307)) (accessed on 15 January 2023)), originating from MSRA, comprises approximately 45,000 sentences, with more than 30,000 instances of geographical placenames. This corpus serves as a significant dataset extensively employed in named entity recognition tasks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Meaning** & **Example** \\ \hline XZQHS & Provincial administrative divisions & \(\blacksquare\) (in English: Henan Province) \\ XZQHCS & Municipal administrative divisions & \(\blacksquare\) (in English: Zhengzhou City) \\ XZQHQX & County (district) administrative divisions & \(\blacksquare\) (in English: Zhongmu County) \\ XZQHZ & Town (township) administrative divisions & \(\blacksquare\) (in English: Baisha Town) \\ XZQHC & Village & \(\blacksquare\) (in English: Gaozhuang Village) \\ JD1 & Road 1 & \(\blacksquare\) (in English: Shangdu Road) \\ JD2 & Road 2 & \(\blacksquare\) (in English: Wansan Highway) \\ JXK & Intersection & \(\blacksquare\) (in English: Intersection) \\ DIR & Direction & \(\blacksquare\) (in English: Northwest) \\ DIS & Distance & 50 m (in English: 50 m) \\ BES & Blur shift & \(\blacksquare\) (in English: Nearby) \\ MP1 & Local area 1 & 489\(\blacksquare\) (in English: No. 489) \\ MP2 & Local area 2 & \(\blacksquare\) (in English: Boshi Jiayuan) \\ MP3 & Local area 3 & 258\(\blacksquare\) (in English: Building 25) \\ POI1 & Point of interest 1 & \(\blacksquare\) (in English: Baisha Trade City) \\ POI2 & Point of interest 2 & \(\blacksquare\) (in English: Chabaidao) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Labeling system of Chinese address elements.