Address matching is an important task for many businesses especially delivery
and take out companies which help them to take out a certain address from their
data warehouse. Existing solution uses similarity of strings, and edit distance
algorithms to find out the similar addresses from the address database, but
these algorithms could not work effectively with redundant, unstructured, or
incomplete address data. This paper discuss semantic Address matching
technique, by which we can find out a particular address from a list of
possible addresses. We have also reviewed existing practices and their
shortcoming. Semantic address matching is an essentially NLP task in the field
of deep learning. Through this technique We have the ability to triumph the
drawbacks of existing methods like redundant or abbreviated data problems. The
solution uses the OCR on invoices to extract the address and create the data
pool of addresses. Then this data is fed to the algorithm BM-25 for scoring the
best matching entries. Then to observe the best result, this will pass through
BERT for giving the best possible result from the similar queries. Our
investigation exhibits that our methodology enormously improves both accuracy
and review of cutting-edge technology existing techniques.

该研究论文介绍了语义地址匹配技术，通过使用 OCR 从发票中提取地址并创建地址数据池，然后使用 BM-25 算法对最佳匹配条目进行评分，最后通过 BERT 进行相似查询以获得最佳结果，研究结果表明该方法显著提高了现有技术的准确性和性能。

自然语言处理中语义地址匹配的改进

Improvement in Semantic Address Matching using Natural Language  Processing

Addresses occupy a niche location within the landscape of textual data, due
to the positional importance carried by every word, and the geographical scope
it refers to. The task of matching addresses happens everyday and is present in
various fields like mail redirection, entity resolution, etc. Our work defines,
and formalizes a framework to generate matching and mismatching pairs of
addresses in the English language, and use it to evaluate various methods to
automatically perform address matching. These methods vary widely from distance
based approaches to deep learning models. By studying the Precision, Recall and
Accuracy metrics of these approaches, we obtain an understanding of the best
suited method for this setting of the address matching task.

通过研究地址匹配任务中的准确率、召回率和准确性指标，我们对地址匹配的最佳方法进行了研究，探究了从基于距离的方法到深度学习模型的各种方法。

匹配英语语言地址的方法

Methods for Matching English Language Addresses

Matching addresses is a critical task for companies and post offices involved
in the processing and delivery of packages. The ramifications of incorrectly
delivering a package to the wrong recipient are numerous, ranging from harm to
the company's reputation to economic and environmental costs. This research
introduces a deep learning-based model designed to increase the efficiency of
address matching for Portuguese addresses. The model comprises two parts: (i) a
bi-encoder, which is fine-tuned to create meaningful embeddings of Portuguese
postal addresses, utilized to retrieve the top 10 likely matches of the
un-normalized target address from a normalized database, and (ii) a
cross-encoder, which is fine-tuned to accurately rerank the 10 addresses
obtained by the bi-encoder. The model has been tested on a real-case scenario
of Portuguese addresses and exhibits a high degree of accuracy, exceeding 95%
at the door level. When utilized with GPU computations, the inference speed is
about 4.5 times quicker than other traditional approaches such as BM25. An
implementation of this system in a real-world scenario would substantially
increase the effectiveness of the distribution process. Such an implementation
is currently under investigation.

采用深度学习的方法，设计出一种模型，用于提高邮地址匹配的效率。测试结果表明，模型在准确性和速度方面都表现良好，对于解决分配过程中的问题具有实际价值。

使用 Siamese Transformer Networks 改善地址匹配

Improving Address Matching using Siamese Transformer Networks

There is evidence that address matching plays a crucial role in many areas
such as express delivery, online shopping and so on. Address has a hierarchical
structure, in contrast to unstructured texts, which can contribute valuable
information for address matching. Based on this idea, this paper proposes a
novel method to leverage the hierarchical information in deep learning method
that not only improves the ability of existing methods to handle irregular
address, but also can pay closer attention to the special part of address.
Experimental findings demonstrate that the proposed method improves the current
approach by 3.2% points.

本文提出了一种基于深度学习的层次化信息利用方法，可以更好地解决不规则地址匹配的问题并更加关注地址中的特殊部分，实验结果表明，该方法可以将现有方法的准确率提高 3.2%。