How to create vectors from text for address matching using binary classification? I want to compare two addresses for their similarity (purely similarity in terms of textual occurrences rather than semantic similarity). I have a labelled dataset having "Address1", "Address2" and "Similar", where "Similar" takes a value of 0 (not similar) or 1 (similar). 

Now, how would I convert the text in "Address1" and "Address2" into numerical vectors, so as to treat this as a binary classification problem?
I would rather recommend to consider each address as a separate instance. (Separate row in feature matrix)

Each address can be further broken into attributes like: House no., Street, City, State, Country.

Above attributes can be vectorised based on distance between them. Like vectorise each country from west to east(incremental numeric value). Then vectorise each state in country from west to east. Similarly for street. In case two entities fall on same longitude, assign consecutive numerical values.

Once each attribute is vectorised, similarity between two addresses can be Euclidean distance between vectors of two addresses.

Anyways leaving aside data science approach, this problem can be solved further simply by getting distance between two addresses using Google Maps API.