Context relevant tagging statements
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I know only this method:

    >>> text = nltk.word_tokenize(input)
    >>> nltk.pos_tag(text)

And I now want basic context-relevant tagging (like 'refuse' here):

    "the refuse of the campers"
    "the campers refuse to"

While the `refuse`es get tagged `NN` and `VBP` respectively, I want to know the **explicit statements** that performed the actions (I suspect that those two functions are **wrapper functions** to something more complex). It is so that I can experiment with different components to check out the results.

I tried `dont begolomenofy me` with those statements and the big word got tagged `NN`. So was `sworn` in `sworn enemy`.


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What are the explicit statements that reproduces the exact functionality of the statements? And what are the components that may perform better? I am thinking of NGram and HMM taggers for reliable results.

I intend to extract phrases, "black dog"  is more valuable than "black" and "dog".