如何将NLP解析树拆分为子句(独立和从属)? [英] How to split an NLP parse tree to clauses (independent and subordinate)?

问题描述

给出

(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))))))))))))) (. .)))

原始句子是:您可以说他们经常去洗个澡，这使他们更加振奋和更加快乐."

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

如何提取这些条款并进行反向工程? 我们将在S和SBAR处进行拆分(以保留子句的类型，例如从属)

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))))))))))))) (. .)))

到达

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

在S和SBAR拆分似乎很容易.问题似乎是从片段中剥离了所有POS标签和大块.

Splitting at S and SBAR seems very easy. The problem seems to be stripping away all the POS tags and chunks from the fragments.

推荐答案

您可以使用Tree.subtrees().有关更多信息，请检查 NLTK树类.

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

代码:

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

输出:

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

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