If by "minimal noun phrases" you mean "single words that happen to be nouns", then yes, a tagger serves to extract those.

So how could I take advantage of this for my purpose?

This depends on what particular things you want to extract that go beyond just the individual words that get "noun" tags. Multi-noun referring expressions (e.g. "corner store", "Perl Hacker")? Phrases that include function words and/or adjectives? Arguments (subjects and/or objects) of verbs?

A typical approach is to start with some text that contains hand-marked examples of the things you want to extract, and then build a statistical model that assigns weights to the various contexts associated with those examples -- that is, to the various patterns of POS tags in and around each chunk to be extracted. Depending on the details of the project and data the models may need to include actual words in the targets and/or contexts as well as the POS tags. (Of course, the more training data you have, the better.)

Then run the model on a separate set of hand-tagged data to see how well it does. If it does reasonably well (not to many misses or false positives), then you're ready to put it to use on real data.