# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 2 Overview of Geocoding and Its Advancement
### Existing Studies on the Advancement of Geocoding and Word Matching Using Machine Learning  
Geocoding has been the subject of multiple studies, particularly on the improvement of its process. [PERSON] (2009) [5] focused on an area-based address geocoding method since street-based address geocoding methods are limited to Western countries and thus, do not apply to countries with area-based addressing systems. The suggested area-based address geocoding method was able to define house locations within a block accurately. In order to do that, this method models the boundaries with block number and building number range information, and the network distance defines the geographic coordinates of houses along segments of block polygons using a linear interpolation technique. Furthermore, a 3D address geocoding method was also proposed based on the 3D indoor geocoding [26]. The 3D address includes an address for building and an indoor address, such as an apartment number. Especially for the 3D indoor geocoding, network models of buildings are ideal. The constructed network model forms the foundation of calculating network distances from an interpolation method and finding location information in a building. [PERSON] et al. (2017) [27] also suggested a novel idea for 3D indoor geocoding that utilizes optical character recognition to detect the current indoor location and semantic queries to determine a destination of interest. [PERSON] et al. (2015) [6] suggested three fuzzy matching algorithms for Chinese address matching based on a full-text search. They focused more on user input and result control rather than address standardizations and models, which current researchers pay attention to. With the three fuzzy matching methods, the address matching engine was able to achieve higher match efficiency than the traditional database retrieval. In addition, it guaranteed greater freedom on user input and result control and showed very high accuracy (100%). [PERSON] and [PERSON] (2018) [14] proposed a method to standardize addresses to improve geocoding results. The address data undergo parsing, semantical analysis, and reformatting through the natural language process. The developed method was tested on 233 primary school addresses using Google Geocoding API and ArcGIS geocoding API. In addition, the test data were standardized in three formats--the Turkish National Post Telephone Telegraph, Google, and ArcGIS. The results indicated that the standardized addresses significantly improved the accuracy of the geocoding results. Especially, when the addresses were standardized in the Google format and geocoded using Google Geocoding API, it showed the highest accuracy (99.1%). Regarding word matching, some studies suggested ways of using machine learning methods [13; 18; 28; 29]. [PERSON] et al. (2006) [28] developed a novel geocode match engine with  
Figure 1: General steps for geocoding. a rule-based approach to find an exact match or other approximate matches. Especially, hidden Markov models were adopted to achieve better address standardization accuracy. The developed geocoding engine achieved 94.94% matches at different levels (address, street, locality). [PERSON] and [PERSON] (2019) [18] proposed an approach to the alias database management for efficient POI (Point of Interest) retrieval. The authors adopted Word2 vec, a simple neural network structure, for word embedding to convert text data into the form of numeric vectors. The word embedding is capable of making a machine learning model understand similar meanings of words. The suggested method determines the match of a given POI name and the corresponding POI in the alias DB based on text similarity. The most similar word with the similarity degree of 60% or more is retrieved, and the user confirms whether it is the correct one. [PERSON] et al. (2018) [29] used supervised machine learning methods for combining multiple similarity metrics concerning toponym matching.