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我正在使用 Python 3.2，我尝试为一个句子构建一个随机生成的解析树。虽然我确定它会生成句子，但我不确定解析树有多随机，而且我不知道是否有改进此代码的更好/更有效的方法。（我是编程和 Python 方面的新手，最近对 NLP 很感兴趣。欢迎提供任何建议、解决方案或更正。）

 N=['man','dog','cat','telescope','park']  #noun
 P=['in','on','by','with']   #preposition
 det=['a','an','the','my']   #determinant
 V=['saw','ate','walked']    #verb
NP=['John','Mary','Bob']    #noun phrase


from random import choice
 PP=choice(NP)+' '+choice(P)   #preposition phrase
 PP=''.join(PP)
 VP=''.join(choice(V)+' '+choice(NP)) or''.join(choice(V)+' '.choice(NP)+(PP)) #verb phrase         
 VP=''.join(VP) #verb phrase 
 S=choice(NP)+' '+VP  #sentence
 print(S)
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1 回答 1

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尝试 NLTK，http://nltk.org/book/ch08.html

import nltk
from random import choice, shuffle, random

# Sometimes i find reading terminals as values into a dict of POS helps.
vocab={
'Det':['a','an','the','my'],
'N':['man','dog','cat','telescope','park'],
'V':['saw','ate','walked'],
'P':['in','on','by','with'],
'NP':['John','Mary','Bob']
}

vocab2string = [pos + " -> '" + "' | '".join(vocab[pos])+"'" for pos in vocab]

# Rules are simpler to be manually crafted so i left them in strings
rules = '''
S -> NP VP
VP -> V NP
VP -> V NP PP
PP -> NP P
NP -> Det N
'''

mygrammar = rules + "\n".join(vocab2string)
grammar = nltk.parse_cfg(mygrammar) # Loaded your grammar
parser =  nltk.ChartParser(grammar) # Loaded grammar into a parser

# Randomly select one terminal from each POS, based on infinite monkey theorem, i.e. selection of words without grammatical order, see https://en.wikipedia.org/wiki/Infinite_monkey_theorem
words = [choice(vocab[pos]) for pos in vocab if pos != 'P'] # without PP
words = [choice(vocab[pos]) for pos in vocab] + choice(vocab('NP')) # with a PP you need 3 NPs

# To make sure that you always generate a grammatical sentence
trees = []
while trees != []:
  shuffle(words)
  trees = parser.nbest_parse(words)

for t in trees:
  print t
于 2013-07-15T08:08:14.483 回答