What is it called, to extract an address from HTML via NLP
Strip the text out of the HTML page (unless there is a way from the  HTML to identify the address text such as div with a particular class) then build a set of rules that match the address formats used.
If there are postal addresses in several countries then the formats may be markedly different but within a country, the format is the same (with some tweaks) or it is not valid.
Within the US, for example, addresses are 3 or 4 lines (including the person). There is usually a zip code (5 digits optionally followed by four more).  Other countries have postal codes in different formats.
Unless your goal is 100% accuracy on all addresses then you probably should aim for extracting as many addresses as you can within the budget for the task.
It doesn't seem like a task for NLP unless you want to use Named Entity identification to find  cities, countries etc.
I have 300k+ html documents, which I want to extract postal addresses from. The data is different structures, so regex wont work.
I have done a heap of reading on NLP and NLTK for python, however I am still struggling on where to start with this.
Is this approach called Part-of-Speech Tagging or Chunking / Partial Parsing? I can't find any document on how to actually TAG a page so I can train a model on it, or even what I should be training.
So my questions;

What is this approach called?
How can I tag some documents to train from