python-2.7 - Python和NLTK : How to analyze sentence grammar?

我有这段代码，应该根据定义的语法显示句子的句法结构。但是，它返回一个空的[]。我想念什么或做错什么？

import nltk

grammar = nltk.parse_cfg("""
S -> NP VP
PP -> P NP
NP -> Det N | Det N PP
VP -> V NP | VP PP
N -> 'Kim' | 'Dana' | 'everyone'
V -> 'arrived' | 'left' |'cheered'
P -> 'or' | 'and'
""")

def main():
    sent = "Kim arrived or Dana left and everyone cheered".split()
    parser = nltk.ChartParser(grammar)
    trees = parser.nbest_parse(sent)
    for tree in trees:
        print tree

if __name__ == '__main__':
    main()

最佳答案

让我们做一些逆向工程:

>>> import nltk
>>> grammar = nltk.parse_cfg("""
... NP -> Det N | Det N PP
... N -> 'Kim' | 'Dana' | 'everyone'
... """)
>>> sent = "Kim".split()
>>> parser = nltk.ChartParser(grammar)
>>> print parser.nbest_parse(sent)
[]

似乎规则甚至不能将第一个作品都识别为NP。因此，让我们尝试注入(inject)NP -> N

>>> import nltk
>>> grammar = nltk.parse_cfg("""
... NP -> Det N | Det N PP | N
... N -> 'Kim' | 'Dana' | 'everyone'
... """)
>>> sent = "Kim".split()
>>> parser = nltk.ChartParser(grammar)
>>> print parser.nbest_parse(sent)
[Tree('NP', [Tree('N', ['Kim'])])]

现在，它开始工作了，让我们继续Kim arrived or Dana and:

>>> import nltk
>>> grammar = nltk.parse_cfg("""
... S -> NP VP
... PP -> P NP
... NP -> Det N | Det N PP | N
... VP -> V NP | VP PP
... N -> 'Kim' | 'Dana' | 'everyone'
... V -> 'arrived' | 'left' |'cheered'
... P -> 'or' | 'and'
... """)
>>> sent = "Kim arrived".split()
>>> parser = nltk.ChartParser(grammar)
>>> print parser.nbest_parse(sent)
[]
>>>
>>> sent = "Kim arrived or".split()
>>> parser = nltk.ChartParser(grammar)
>>> print parser.nbest_parse(sent)
[]

似乎无法获取带有或不带有VP的P，因为V之后需要NP，或者在获取VP之前必须先走到树上成为P，因此放宽规则并改为使用VP -> V PP即可的VP -> VP PP:

>>> import nltk
>>> grammar = nltk.parse_cfg("""
... S -> NP VP
... PP -> P NP
... NP -> Det N | Det N PP | N
... VP -> V NP | V PP
... N -> 'Kim' | 'Dana' | 'everyone'
... V -> 'arrived' | 'left' |'cheered'
... P -> 'or' | 'and'
... """)
>>> sent = "Kim arrived or Dana".split()
>>> parser = nltk.ChartParser(grammar)
>>> print parser.nbest_parse(sent)
[Tree('S', [Tree('NP', [Tree('N', ['Kim'])]), Tree('VP', [Tree('V', ['arrived']), Tree('PP', [Tree('P', ['or']), Tree('NP', [Tree('N', ['Dana'])])])])])]

好的，我们越来越近了，但是接下来的单词似乎又打破了cfg规则:

>> import nltk
>>> grammar = nltk.parse_cfg("""
... S -> NP VP
... PP -> P NP
... NP -> Det N | Det N PP | N
... VP -> V NP | V PP
... N -> 'Kim' | 'Dana' | 'everyone'
... V -> 'arrived' | 'left' |'cheered'
... P -> 'or' | 'and'
... """)
>>> sent = "Kim arrived or Dana left".split()
>>> parser = nltk.ChartParser(grammar)
>>> print parser.nbest_parse(sent)
[]
>>> sent = "Kim arrived or Dana left and".split()
>>> parser = nltk.ChartParser(grammar)
>>> print parser.nbest_parse(sent)
[]
>>>
>>> sent = "Kim arrived or Dana left and everyone".split()
>>> parser = nltk.ChartParser(grammar)
>>> print parser.nbest_parse(sent)
[]
>>>
>>> sent = "Kim arrived or Dana left and everyone cheered".split()
>>> parser = nltk.ChartParser(grammar)
>>> print parser.nbest_parse(sent)
[]

因此，我希望上面的示例向您展示，尝试更改规则以从左到右合并语言现象很困难。

而不是从左到右，实现

[[[[[[[[Kim] arrived] or] Dana] left] and] everyone] cheered]

您为什么不尝试制定更合理的语言规则来实现:

[[[Kim arrived] or [Dana left]] and [everyone cheered]]

[[Kim arrived] or [[Dana left] and [everyone cheered]]]

尝试以下方法:

import nltk
grammar = nltk.parse_cfg("""
S -> CP | VP
CP -> VP C VP | CP C VP | VP C CP
VP -> NP V
NP -> 'Kim' | 'Dana' | 'everyone'
V -> 'arrived' | 'left' |'cheered'
C -> 'or' | 'and'
""")

print "======= Kim arrived ========="
sent = "Kim arrived".split()
parser = nltk.ChartParser(grammar)
for t in parser.nbest_parse(sent):
    print t

print "\n======= Kim arrived or Dana left ========="
sent = "Kim arrived or Dana left".split()
parser = nltk.ChartParser(grammar)
for t in parser.nbest_parse(sent):
    print t

print "\n=== Kim arrived or Dana left and everyone cheered ===="
sent = "Kim arrived or Dana left and everyone cheered".split()
parser = nltk.ChartParser(grammar)
for t in parser.nbest_parse(sent):
    print t

[out] :

======= Kim arrived =========
(S (VP (NP Kim) (V arrived)))

======= Kim arrived or Dana left =========
(S (CP (VP (NP Kim) (V arrived)) (C or) (VP (NP Dana) (V left))))

=== Kim arrived or Dana left and everyone cheered ====
(S
  (CP
    (CP (VP (NP Kim) (V arrived)) (C or) (VP (NP Dana) (V left)))
    (C and)
    (VP (NP everyone) (V cheered))))
(S
  (CP
    (VP (NP Kim) (V arrived))
    (C or)
    (CP
      (VP (NP Dana) (V left))
      (C and)
      (VP (NP everyone) (V cheered)))))

上面的解决方案显示了CFG规则如何足够健壮，不仅要捕获完整的句子，还要捕获句子的一部分。

关于python-2.7 - Python和NLTK : How to analyze sentence grammar?，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/20983494/