## 2 Related Works
The research makes use of attention mechanisms to assess the relative importance of the various components of the address and the Gated Recurrent Unit (GRU) to learn the intrinsic link in the Chinese address. Without requiring extra information, such as a standard address database or a geological element table, the method can fill in the blanks in the administrative address information and fix incorrect address information. Other scholars [18; 19; 20] also conducted in-depth research to carry out the difficulty of address standardization with various aspects. Another study by [PERSON] et al. [21] proposed an address geocoding system that makes use of machine learning to improve the street-based address-matching method. Address parsing, address matching, and address locating are the three modules that make up the developed address geocoding algorithm. The input addresses are divided into components of the street-based system using a regex-based parsing approach. This paper provides a method to integrate similarity measures for address matching in order to enhance performance. A study by [PERSON] et al. [22] aimed to create a system that matches free-text address data with conventional addresses using the Support Vector Machines method. A free text address's resemblance to a standard address is expressed as a numerical value by a model that was trained using categorized data. The research confirmed that by applying the proposed system to the free-text addresses generated from 250,000 addresses, the system could achieve a matching accuracy of over 81%. [PERSON] et al. [23] introduced an address-matching technique based on deep transfer learning to identify semantic similarities between various addresses. The proposed study firstly pre-trained the address corpus to learn address contexts unsupervised and then created a labeled address-matching dataset that enables the matching problem to be transformed into a binary classification prediction problem by utilizing the specific geographic feature. The study finally applied the fine-tuning technique by using the address-matching dataset to build the classification model. The results showed that their model performed at the highest level, with precision, recall, and F1 score above 0.98. Another similar study by [PERSON] et al. [24] proposed a geographical address representation learning for address matching. The proposed research learned the geographical semantic representations for address strings by obtaining rich contexts for addresses from the Web via search engines. The study utilized an encoder-decoder architecture to learn semantic vector representation for each address string along with the attention mechanism and then constructed a large graph from the corpus containing address elements and addresses as nodes. Word co-occurrence data are used to build the edges between nodes in order to develop embedding representations for every node on the graph. The study claimed that their proposed method outperformed the existing methods in terms of precision and recall. Meanwhile, [PERSON] et al. [25] also applied a deep-learning-based architecture for semantic address matching in their research. The study trained the word2 vec model to convert the address data into their respective vector representations and then applied the enhanced sequential inference model to make inferences to determine the matching of two addresses. The research used real-world address data from the Shenzhen Address Database to evaluate the proposed method. The result showed that the proposed method gained a higher matching accuracy for unstructured address data, with its precision, recall, and F1 score up to 0.97. Another study [26; 27; 28; 29; 30; 31] from related scholars also proposed different methodologies to deal with address-matching problems in various contexts by utilizing the most advanced technologies, such as deep learning and machine learning. All studies significantly proved the results in their own way.