## 2 Related Work
### Sorting and Combination of Chinese Addresses Based on FSM  
FSMs [37] are models for directed graphs used to study the computation processes of finite states. They consist of a finite state set, input set, and state transition rule set. The finite state set describes the system state, the input set represents the input information of the system, and the state transition rule set describes the transition conditions between states. The FSM is widely used in fields such as computer science, linguistics, logic, and mathematics. Owing to its good flexibility and scalability, the FSM can adapt to different types and forms of address elements and quickly process large amounts of text data because they only require a small amount of calculation. Therefore, FSMs are widely used for parsing Chinese address elements. Currently, many Chinese address-sorting and combination methods based on the FSM have been implemented. Certain results have been obtained using these methods. For example, because elements in address descriptions that comply with regular patterns may appear disordered, incomplete, or inconsistent with cognitive habits, [PERSON] [38] used a method based on pattern matching integrated with address element vocabulary to establish a rule set for address description patterns. Integrated with the bidirectional FSM, the recognized address information was sorted and combined according to the hierarchical elements; therefore, the obtained address was standardized. [PERSON] et al. [39] proposed a new method based on an address hierarchy classification driven by FSM. First, this method removed redundant noise words, such as punctuation marks, notes, and localizers, from the original address. Subsequently, a general word segmentation software was used to preliminarily segment the words. Then, an algorithm based on feature word recognition using a classification model driven by an FSM for address hierarchy classification, recognition, labeling, and standardized coding was developed. This method could effectively solve the difficult problem of Chinese address standardization. [PERSON] et al. [40] proposed the T-FA model, which uses natural language processing to address administrative regions based on the Trie model and the finite state automaton model to extract elements of non-standard addresses. This method performed better at processing addresses in batches. [PERSON] [41] adopted a unique strategy that deviates from the conventional method of assigning individual characters as weights to arcs within an FSM; on the contrary, their method utilizes administrative unit names from addresses as the arc weights. This innovative approach not only diminished the space complexity of the algorithm's implementation but also enhanced its execution speed. As a result, it successfully facilitated the recognition of Chinese addresses. Although the methods of sorting and combining Chinese addresses based on FSM have achieved certain results, they cannot process scenarios where the address elements are completely disordered or missing. In addition, traditional FSM relies heavily on the included vocabulary or requires a threshold setting, which reduces its generalizability for address sorting and combination. Therefore, this study proposes a dynamic FSM algorithm to improve element sorting and the combination of non-standard addresses in Chinese. Considering the classification system of address elements and the characteristics of Chinese address descriptions, the method of combining address elements becomes more complex with the gradual refinement of the granular address descriptions, and the order of elements may be omitted or reversed. Therefore, after the address elements are recognized using the deep learning model, the dynamic FSM is further needed to sort and parse the address elements to address the disorder and missing hierarchical elements in the description of address information in the text. Consequently, addresses with correct hierarchical structures can be obtained for subsequent high-accuracy address matching.