  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.
