How to easily get VP/NP/PP phrase?

[bayesrule]

Hi,

I'm new to this great tool. Besides successful parsing, is there any convenient way to get some useful elements (e.g. none phrases in the input) from the parsing result?

[yzhangcs]

@bayesrule Hi, the results predicted by the parser are just stored in the form of nltk.Tree.
So you can access nonterminals simply via nltk APIs:

>>> par = Parser.load('crf-con-en')
>>> tree = par.predict("She enjoys playing tennis .".split())[0].trees
>>> tree.productions()
[TOP -> S, S -> NP VP _, NP -> _, _ -> 'She', VP -> _ S, _ -> 'enjoys', S -> VP, VP -> _ NP, _ -> 'playing', NP -> _, _ -> 'tennis', _ -> '.']

Besides, supar also provides some useful fns for factorization:

>>> supar.utils.Tree.factorize(tree)
[(0, 5, 'TOP'), (0, 5, 'S'), (0, 1, 'NP'), (1, 4, 'VP'), (2, 4, 'S'), (2, 4, 'VP'), (3, 4, 'NP')]

[github-actions]

This issue is stale because it has been open for 30 days with no activity.

[github-actions]

This issue was closed because it has been inactive for 7 days since being marked as stale.