Joint Learning for Non-standard Chinese Building Address Standardization

Xi, Xuefeng; Wang, Lei; Zou, Encen; Zeng, Cheng; Fu, Baochuan

doi:10.1109/isc2.2018.8656953

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…The research by Xi 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.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data

et al. 2022

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Validating and improving the quality of global address data are important tasks in a modern society where exchanges between countries are due to active Free Trade Agreements (FTAs) and e-commerce. Addresses may be constructed with different systems for each country; therefore, to verify and improve the quality of the address data, it is necessary to understand the address system of each country in advance. In the event of food risk, it is important to identify the administrative district from the address in order to take safety measures, such as predicting the contaminated area by tracking the distribution of food in the area. In this study, we propose a method that applies a deep learning approach to verify and improve the quality of the global address data required for imported food-safety management. The address entered by the user is classified to the administrative division levels of the relevant country and the quality of the address data is verified and improved by converting them into a standardized address. Finally, the results show that the accuracy of the model is found to be approximately 90% and the proposed method is able to verify and evaluate the overseas address data quality significantly.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data

et al. 2022

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“…In this paper we describe a solution for the address verification problem: we propose a cleansing and address validation process, in providing (1) a categorization of dirt (e.g. typos, misspelling, geographic inconsistencies), (2) an address standardization method , and finally (3) an address classification method.…”

Section: Introductionmentioning

confidence: 99%

“…In the industry, software vendors such as Experian 1 or Informatica 2 are expanding their businesses in providing address verification/validation solutions. In the academia, some research has focused on address cleansing solutions, including preprocessing and parsing, especially for structured addresses [1][2][3]. Address validation was usually performed based on geocoding solutions such as geocoding APIs (e.g.…”

Section: Introductionmentioning

confidence: 99%

A RoBERTa Based Approach for Address Validation

Guermazi

Sellami

Boucelma

2022

Communications in Computer and Information Science

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Address verification is becoming more and more mandatory for businesses involved in parcel or mail delivery. Situations where shipments are returned or delivered to the wrong person (or legal entity) are harmful and may incur several costs to the stakeholders. Indeed, addresses often carry incorrect information that need to be corrected prior to any shipment process. In this paper we propose a 2-step address validation approach consisting of (1) standardization and (2) classification, both steps are based on RoBERTa, a pre-trained language model. Experiments have been conducted on real datasets and demonstrate the effectiveness of the approach in comparison to other methods.

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Joint Learning for Non-standard Chinese Building Address Standardization

Cited by 2 publications

References 4 publications

Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data

Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data

A RoBERTa Based Approach for Address Validation

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