Methods for Matching English Language Addresses
This work addresses the niche but important problem of address matching for applications like mail redirection and entity resolution, though it is incremental as it formalizes and evaluates existing methods rather than introducing new ones.
The paper tackled the problem of automatically matching English language addresses by defining a framework to generate matching and mismatching pairs and evaluating various methods, including distance-based approaches and deep learning models, with results analyzed using Precision, Recall, and Accuracy metrics to identify the best-suited method.
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.