利用子字嵌入进行跨国地址解析
Leveraging Subword Embeddings for Multinational Address Parsing
摘要：
地址解析包括标识组成地址的段，如街道名称或邮政编码。由于地址解析对于像记录链接这样的任务的重要性，已经有许多技术来处理它。神经网络方法为地址解析定义了一种新的最先进的方法。虽然这种方法取得了显着的成果，以前的工作只集中在应用神经网络，以实现从一个来源国的地址解析的地址。我们提出了一种方法，在该方法中，我们采用子字嵌入和递归神经网络架构，以建立一个单一的模型，能够学习解析来自多个国家的地址在同一时间，同时考虑到语言和地址格式系统的差异。我们在用于培训的国家实现了约99%的准确率，无需预处理或后处理。我们探讨的可能性，通过培训获得的地址解析知识的一些国家的地址转移到其他没有进一步的培训，在zero-shot迁移学习设置。我们在80%的国家（41个国家中的33个国家）取得了良好的成绩，其中近50%（41个国家中的20个国家）的性能接近最先进水平。此外，我们提出了一个开源的Python实现我们的训练模型。
英文摘要：
Address parsing consists of identifying the segments that make up an address such as a street name or a postal code. Because of its importance for tasks like record linkage, address parsing has been approached with many techniques. Neural network methods defined a new state-of-the-art for address parsing. While this approach yielded notable results, previous work has only focused on applying neural networks to achieve address parsing of addresses from one source country. We propose an approach in which we employ subword embeddings and a Recurrent Neural Network architecture to build a single model capable of learning to parse addresses from multiple countries at the same time while taking into account the difference in languages and address formatting systems. We achieved accuracies around 99 % on the countries used for training with no pre-processing nor post-processing needed. We explore the possibility of transferring the address parsing knowledge obtained by training on some countries' addresses to others with no further training in a zero-shot transfer learning setting. We achieve good results for 80 % of the countries (33 out of 41), almost 50 % of which (20 out of 41) is near state-of-the-art performance. In addition, we propose an open-source Python implementation of our trained models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
