## 2 Related Works  
In modern society, addresses are becoming a basic infrastructure, going beyond the concept of residence, and are connected to all industries, such as logistics, postal, e-commerce, and location-based industries. The international community is also establishing the address as an international standard to reduce the cost of distribution systems throughout the industry. Recently, the scope of standard enactment has been expanded to address quality, exchange, and maps, and in the IoT era, it is expanding to the standardization of object addresses. Figure 1 shows an example of address verification. Address verification takes an address string as input and converts it into a correct address string through parsing, formatting, standardization, geocoding, and verification processes (refer to Figure 1) [7]. [PERSON] et al. [8] detailed address tags, which were constructed based on the Australian National Postal Address Guidelines and the Australian Geocoded National Address File (G-NAF). They also presented an automated approach for address cleaning and standardization using a probabilistic hidden Markov model (HMM). Figure 2 delivers an example of the address tag [8]. [PERSON] et al. [9] proposed Deepparse, a deep-learning-based multinational address parsing library that can be applied to non-standard address problems, named entity recognition (NER) problems. The library suggested an improved generalization technique for non-standard address data and a solution to the class mixing and entity name recognition problems. [PERSON] et al. [10] presented a machine-learning-based technique for address parsing using a neural network. Similarly, [PERSON] et al. [11] used a deep learning approach to apply to address parsing tasks in their research by utilizing the convolutional neural network. Another study, by [PERSON] et al. [12], offered a Hidden-Markov-Model-based approach to parse the addresses by building the models based on the synthetic training data. [PERSON] et al. [13] proposed a method for detecting location information of an extended concept that includes administrative districts, names of institutions, libraries, and movie theaters in text data that are not geo-tagged. Using unstructured text data extracted from news, articles, blogs, and social media, a deep learning model based on labeling, word  
Figure 1: Address verification [7]. Figure 2: Detailed Australian address tag—an example. embedding, and attention was used to create a binary classifier and predict whether place information was included. [PERSON] and [PERSON] [14] proposed a technique for standardizing addresses using natural language processing for improving geocoding results. The research addressed problems, such as inaccurate numbering systems, misspellings, the use of abbreviations, and a lack of data by way of a standardization process that decomposes addresses used as input data in geocoding by identifying spelling mistakes and abbreviations and reorganizing the address via natural language processing. [PERSON] et al. [15] provided a RoBERTa-based approach to validate the address. The research utilized a two-step address verification approach consisting of standardization and classification. Both steps depended on RoBERTa, which is a pre-trained language model. After the experiments on the real dataset, the result showed the effectiveness of the proposed technique compared to the alternative approaches. The research by [PERSON] et al. [16] suggested an original joint learning strategy based on the hash map principle and word frequency theory to standardize Chinese non-standard building addresses. The proposed research was to address the issues of using traditional methods based on string matching that struggles to meet the task requirements because of the substantial number of non-standard building addresses and the semantic ambiguity of addresses stated in the Chinese natural language. [PERSON] et al. [17] suggested a way for standardizing addresses for Chinese addresses based on the seq2 seq model.