How to split an NLP parse tree to clauses (independent and subordinate)?

Question

Given an NLP parse tree like

(ROOT (S (NP (PRP You)) (VP (MD could) (VP (VB say) (SBAR (IN that) (S (NP (PRP they)) (ADVP (RB regularly)) (VP (VB catch) (NP         


        

Answer 1

You can use Tree.subtrees(). For more information check NLTK Tree Class.

Code:

from nltk import Tree

parse_str = "(ROOT (S (NP (PRP You)) (VP (MD could) (VP (VB say) (SBAR (IN that) (S (NP (PRP they)) (ADVP (RB regularly)) (VP (VB catch) (NP (NP (DT a) (NN shower)) (, ,) (SBAR (WHNP (WDT which)) (S (VP (VBZ adds) (PP (TO to) (NP (NP (PRP$ their) (NN exhilaration)) (CC and) (NP (FW joie) (FW de) (FW vivre))))))))))))) (. .)))"
#parse_str = "(ROOT (S (SBAR (IN Though) (S (NP (PRP he)) (VP (VBD was) (ADJP (RB very) (JJ rich))))) (, ,) (NP (PRP he)) (VP (VBD was) (ADVP (RB still)) (ADJP (RB very) (JJ unhappy))) (. .)))"

t = Tree.fromstring(parse_str)
#print t

subtexts = []
for subtree in t.subtrees():
    if subtree.label()=="S" or subtree.label()=="SBAR":
        #print subtree.leaves()
        subtexts.append(' '.join(subtree.leaves()))
#print subtexts

presubtexts = subtexts[:]       # ADDED IN EDIT for leftover check

for i in reversed(range(len(subtexts)-1)):
    subtexts[i] = subtexts[i][0:subtexts[i].index(subtexts[i+1])]

for text in subtexts:
    print text

# ADDED IN EDIT - Not sure for generalized cases
leftover = presubtexts[0][presubtexts[0].index(presubtexts[1])+len(presubtexts[1]):]
print leftover

Output:

You could say 
that 
they regularly catch a shower , 
which 
adds to their exhilaration and joie de vivre
 .

Answer 2

First get parse tree:

# stanza.install_corenlp()

from stanza.server import CoreNLPClient

text = "Joe realized that the train was late while he waited at the train station"

with CoreNLPClient(
        annotators=['tokenize', 'pos', 'lemma', 'parse', 'depparse'],
        output_format="json",
        timeout=30000,
        memory='16G') as client:
    output = client.annotate(text)
    # print(output.sentence[0])
    parse_tree = output['sentences'][0]['parse']
    parse_tree = ' '.join(parse_tree.split())

Then use this gist to extract clauses by calling:

print_clauses(parse_str=parse_tree)

The output will be:

{'the train was late', 'he waited at the train station', 'Joe realized'}