为nltk解析树生成语法规则 [英] generating grammar rules for nltk parse trees

问题描述

如果我有句子"Mary saw a dog"和以下内容:

If I have the sentence "Mary saw a dog" and the following:

pos_tags = ['NNP', 'VBD', 'DT', 'NN']

是否可以为此句子生成语法规则，以便生成解析树(下面的语法是使用nltk.parse_cfg的语法规则)

Is it possible to generate the grammar rules for this sentence so that a parse tree can be generated (grammar below are the grammar rules using nltk.parse_cfg)

sent = "Mary saw a dog".split()
rd_parser = nltk.RecursiveDescentParser(grammar)

for tree in rd_parser.nbest_parse(sent):
    print tree

推荐答案

您可以尝试:

import nltk
# Define the cfg grammar.
grammar = nltk.parse_cfg("""
S -> NP VP
NP -> 'DT' 'NN'
VP -> 'VB'
VP -> 'VB' 'NN'
""")


# Make your POS sentence into a list of tokens.
sentence = "DT NN VB NN".split(" ")

# Load the grammar into the ChartParser.
cp = nltk.ChartParser(grammar)

# Generate and print the nbest_parse from the grammar given the sentence tokens.
for tree in cp.nbest_parse(sentence):
    print tree

但是正如@alexis强调的那样，您要的是几乎不可能的=)

But as @alexis highlighted, what you're asking for it rather impossible =)

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