Chinese Address Standardization Based on seq2seq Model

Lu, Yan; Liu, Hongli; Zhou, Yanquan

doi:10.1145/3372422.3372441

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Lu et al [17] suggested a way for standardizing addresses for Chinese addresses based on the seq2seq model. 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.…”

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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“…Lu et al used a seq2seq recuurent neural network (RNN) based encoder-decoder architecture with bahdanau attention. They used the gated recurrent unit (GRU) in the RNN block (Lu, 2019). Probabilistic approaches have been used as well.…”

Section: Introductionmentioning

confidence: 99%

Last Mile Logistics: Impact of Unstructured Addresses on Delivery Times

Rahman

Basheer

Khalid

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The e-commerce industry has seen significant growth over the past few years. One significant issue that has sprung up as a result of this growth is unstructured addresses during last mile delivery. These ambiguous addresses are an established issue, particularly in developing countries like Pakistan. They are difficult to read and locate by last mile delivery riders thereby increasing delivery times and cost, negatively impacting the business of the company. Increased delivery times are also detrimental to the environment. In this paper, we aim to quantify the effects of unstructured addresses on last mile logistics. Many attempts have been made to standardise addresses to tackle this problem. Deep learning based approaches using recurrent neural networks (RNN) as well as probabilistic approaches using hidden Markov models (HMM) have been used. However, the main downside to these approaches are the underlying variation in address schemes in housing societies. We present an end to end rule based pipeline using Levenshtein distance (LD) and regular expressions (RegEx) rules which takes those unstructured addresses and outputs their structured forms along with their Geo-coordinates. The pipeline also returns the optimized route to minimize the last mile distance traveled.

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Chinese Address Standardization Based on seq2seq Model

Cited by 2 publications

References 2 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

Last Mile Logistics: Impact of Unstructured Addresses on Delivery Times

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