Reputation: 4209
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 (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))))))))))))) (. .)))
Original sentence is "You could say that they regularly catch a shower, which adds to their exhilaration and joie de vivre."
How could the clauses be extracted and reverse engineered? We would be splitting at S and SBAR (to preserve the type of clause, eg subordinated)
- (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))))))))))))) (. .)))
to arrive at
- You could say
- that they regularly catch a shower
- , which adds to their exhilaration and joie de vivre.
Splitting at S and SBAR seems very easy. The problem seems to be stripping away all the POS tags and chunks from the fragments.
Upvotes: 8
Views: 8494
Reputation: 16607
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'}
Upvotes: 0
Reputation: 3153
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
.
Upvotes: 11