Question

我正在使用Python 3.2，我试图为一个句子构建一个随机生成的解析树。虽然我确定它生成了句子，但我不确定解析树的随机性如何，我也不知道是否有一种更好/更有效的方法来改进此代码。（我是编程和Python的新手，我最近对NLP感兴趣。欢迎任何建议，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)

Answer 1

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

使用一组固定词汇表随机生成Parse Tree

1 个答案: