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【2020 Application Example】 AI Address Parsing, No More Hitting Walls in Searching for Coordinates

Empower addresses with spatial coordinates to help drive the 'Open Data' policy

In recent years, the government has been promoting 'Open Data' hoping that the openness of data will facilitate inter-agency data flow, enhance administrative efficiency, meet public needs, and strengthen public oversight of the government. Among them, transportation data is closely related to daily life, often reported by the public with the incidents specifying obvious local landmarks or addresses; there have also been public feedback about the traffic reports on police radio that lacked actual coordinates. Introducing these addresses, which were originally without spatial attributes, into the geographical coordinate system is one step toward 'Smart Spatial Decision Making'.

However, unstructured addresses, without manual intervention to improve the inconsistency of address formats, do not yield high location accuracy, necessitating an improvement in data quality and usability to unlock the potential applications of open data. This further aids in policy promotion and widespread application to different sectors including tourism, employment, birth and adoption.

Unregulated and diverse writing styles of addresses lead to low location accuracy

Address Locator is jointly developed by SongXu Information Co., Ltd. and YanDing Intelligent Co., Ltd. GOLiFE as a 'stand-alone address locating software' providing single or batch address location services. To imbue address data with spatial attributes, the core technology of Address Locator involves 'Address Parsing' and 'Address Location' in two stages. Initially, 'Address Parsing' distributes the addresses aimed for positioning according to administrative region hierarchy keywords: province/city, township/district, village, road/street, alley, lane, number; subsequently, 'Address Location' matches the split addresses with the parent address to obtain the location level and corresponding coordinates.

However, in the actual business integration process, since address sources are maintained separately by different authorities, a lack of consistent standards remains a common issue. Problems include: special characters (at address examples in specific regions), omitted administrative units, repetitive administrative hierarchical keywords, special street-alley segments, mismatch in Chinese numericals vs. Arabic numerals, and non-current addresses leading to complex address formats that are difficult to accurately split.

Establishing an address tokenization model, achieving precise location alignment!

To effectively handle various messy address formats and alleviate the difficulties in location alignment for the existing Address Locator, AI and Natural Language Processing technologies are implemented for 'Address Normalization' and a 'Chinese Tokenization Tool' to optimize existing address location capability. 'Address Normalization' addresses the issues of missing keywords, variant character forms, and missing administrative areas; whereas 'Chinese Tokenization Tool' helps resolve 'split errors' caused by special address formats, preventing unsuccessful positioning.

Successful address parsing through AI tokenization technology

▲ Successful address parsing through AI tokenization technology

In the past, while handling address location services, manual preprocessing for data standardization was required, hence it was not solely marketed as a product, but included in project plans that offered address location services. However, after incorporating address normalization and AI tokenization technology, it has become a complete product, significantly reducing the time users spend on manual adjustments and achieving the intended location accuracy. Furthermore, the AI-enhanced Address Locator is now introduced on the SongXu Information Co. Ltd. website, including product descriptions and official listings.

After four months of testing and modifications, AI technology was successfully incorporated into the existing address location product. From selecting the tokenization tools, building the corpus, training the model, and interfacing with product features, to complete test planning, collection from 'Government Data Open Platform' and 'Taichung City Government Data Open Platform,' including over 62 datasets and more than 300,000 addresses, achieving a complete match rate of 90.08% and a fuzzy match rate of 98%, greatly surpassing the original product in match rates and processing time!

To promote AI technology applications in the information services sector, the AI-enhanced address location service is positioned as a new solution and showcased on the SongXu company website; starting from product function introductions, explaining address regularization methods and address location features; subsequently, guiding potential customers to envision applicable scenarios including: decision analytics, precision marketing, and other applications. The product will aid various sectors’ data by assigning spatial information to addresses, delving into the context and trends of data in two-dimensional space.

Address Location Solution

▲ Address Location Solution

Providing spatial coordinates for attractions, intersections, and points of interest

Successful development and implementation of AI-enhanced products in companies focused on smart transportation systems in the domestic market revealed that, while effectively solving address location issues, they also recognized that descriptions of spatial information, beyond addresses inclusive. During their progress, integrating AI more broadly into 'Entity Recognition' is set to be an important future application not limited to address location. In an era of information overload, collecting data is straightforward; identifying keywords of interest is key. Future development directions aim to optimize these products and create more business opportunities!

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-05-19」

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【解決方案】佐翼科技無人機導入高爾夫球場域 可節省一半人力
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internal and external resources Only then will we be able to gradually achieve the goal of making golf courses smarter and smoothly assist the industry with transformation Zuoyi Technology's CEO, Liu Junlin 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【導入案例】哈瑪星科技建構AI模型管理平台 加速AI落地應用
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這是一張圖片。 This is a picture.
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demand convenient life, food consumption patterns value dining experiencesimple and fast, and are equipped with mobile phone-connected ordering models, and hot drinks and Fresh food delivery is the focus of two major trends The location, items sold, consumption methods and multiple payment methods are the focus of market growth for smart vending machines In terms of convenience, Taiwanese consumers still prefer to purchase vending machine food near stations, airports, schools, and businesses in business districts Various payment methods are also gaining more support from consumers, indicating that in the future, automatic Vending machines can be developed in two directions diversified items and diversified payment methods AI sales forecast technology integrates back-end management to achieve precise marketing purposesDue to the wide variety of products, it is difficult to know the performance of products under different factors such as season, market conditions , promotional activities, etc, it is easy to cause out-of-stock or over-inventory situations Magpie Life has specially developed "AI sales forecasting technology" and integrated it into the back-end management system, hoping to lock in customer purchasing preferences and intentions through data analysis In order to achieve the purpose of precise marketing, make accurate business decisions and effectively allocate limited resources The introduction of AI systems can achieve the three major goals of precise marketing, inventory management and supply chain management This system is a replenishment decision-making aid designed specifically for supply chain managers It uses AI to predict future sales demand, helping companies effectively optimize production capacity, inventory and distribution strategies Its overall system architecture includes1 Data exploratory analysis function Provides automatic value filling, automatic coding and automatic feature screening functions for missing values in the data 2 Modeling function 1 Provides model training functions for two types of prediction problems regression Regression and time series Time Series Forecast nbsp2 Supports Auto ML automatic modeling, and the best model is recommended by the system Integrated models can also be established to improve model accuracy nbsp3 Supports multiple algorithm types Random Forest, XGBoost, GBM and other algorithms nbsp4 Supports a variety of time series models exponential smoothing, ARIMA, ARIMAX, intermittent demand, dynamic multiple regression and other models nbsp5 Supports a variety of model evaluation indicators R, MAE, MSE, RMSE, Deviance, AUC, Lift top 1, Misclassification and other indicators nbsp6 Supports automatic cutting of training data sets and Holdout verification data sets, and can manually adjust the ratio nbsp7 Supports automatic model ensemble learning Stacked Ensemble, balancing function learning Balancing Classes, and Early Stopping nbsp8 Supports the creation of multiple models at the same time The system will allocate resources according to modeling needs, so that modeling, prediction and other tasks have independent computing resources and do not affect each other In the overall server space With an upper limit, computing resources can be used efficiently nbsp9 It has in-memory computing function, which can use large-capacity and high-speed memory to perform calculations to avoid reading and writing a large number of files from the hard disk and improve computing performance 3 Data concatenation function Using API grafting and complete data concatenation automation, there is no need to manually import data, improving user experience 4 Chart analysis function Provides visual charts and basic statistical values for product sales AI data analysis solutions have two major advantages 1 Entrepreneurship machines can be rented and sold at low cost to open unmanned physical stores and cooperate with the chain retail industry Through smart machines, entrepreneurs can rent and sell them at a lower cost than the store rent Cost of running a retail business Two cooperation models, machine sales and leasing, are provided, and the choice is based on the evaluation of the industry 2 Various types of products are put on the shelves Products are sold anytime and anywhere 24 hours a day Up to 60 kinds of diversified products can be put on the shelves Large transparent windows enhance the visibility of products Regular replenishment and tracking of product sales status are available, and product types can be adjusted according to needs Recently, the line between the Internet and the physical world has blurred, the way customers interact has changed significantly, and consumer demand is changing and personalized The retail industry is facing unprecedented challenges and uncertainties, and mastering data has become key AI data analysis solutions can help the retail industry quickly activate large amounts of data, create seamless personalized experiences, optimize the operational value chain and improve efficiency, and strengthen the core competitiveness of enterprises 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」