Abstract
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.
Abstract (translated)
地址匹配对于许多企业来说特别是送餐和外卖公司，帮助他们从数据仓库中提取特定地址。现有解决方案使用字符串的相似性和编辑距离算法来查找地址数据库中的类似地址，但这些算法对于冗余、无结构或未完整地址数据无法有效工作。本文讨论了语义地址匹配技术，通过它可以从可能的地址列表中找到特定地址。我们还回顾了现有实践及其不足之处。语义地址匹配是深度学习领域中一个基本的语言处理任务。通过这种技术，我们能够克服现有方法中冗余或缩写数据问题的缺点。解决方案使用发票上的OCR提取地址并创建地址数据池。然后将该数据输入到算法BM-25中进行评分，以观察最佳结果。为了观察最佳结果，这还将通过BERT进行处理，从而从类似查询中获得最佳结果。我们的研究结果表明，我们的方法大大提高了现有技术的准确性和尖端技术的审查。
URL
https://arxiv.org/abs/2404.11691
