将NLTK块转换为词典列表

问题描述

我需要从下面的示例nltk树中获取字典列表：

   (S
      I/PRP
      'll/MD
      have/VB
      (amount 1/CD)
      (plate pizza/NN)
      and/CC
      (amount 4/CD)
      (plate sandwiches/NNS))

所需的输出如下

  [{amount: 1,plate: pizza},{amount: 4,plate: sandwiches}]

我尝试了下面的代码，但只得到一个字典列表： [{数量：4，盘子：三明治}] 列表似乎没有追加新条目，只是更新了同一词典。

import nltk
from nltk.chunk import *
from nltk.chunk.util import *
from nltk.chunk.regexp import *
from nltk import Tree

training = []
hmm_tagger = HiddenMarkovModelTagger.train(training)
sentence = "I'll have 1 pizza and 4 sandwiches"
gram = r"""
plate:      {<NN|NNS>}                    
amount:    {<CD|DT>}
        """
cp = nltk.RegexpParser(gram)
for sent in sentence:
    tokens = nltk.word_tokenize(sent)
    taggex = hmm_tagger.tag(tokens)
treee = cp.parse(taggex)
iob_ts = tree2conlltags(treee)
tree = conlltags2tree(iob_ts)
def conversion(tree):
    dlist = []
    for leaf in tree:
        if type(leaf) == tuple:
            for leaf in tree:
                key = leaf.label()
                value = leaf[0][0]
                dlist =[dict(zip(key,value)) for leaf in tree]              
    return dlist

解决方法

这里的主要问题是在conversion函数的内部和外部，每次循环迭代后您都不会追加。

from nltk.chunk.regexp import RegexpParser
from nltk import Tree,pos_tag
from nltk.tokenize import word_tokenize,sent_tokenize

gram = r"""
plate:      {<NN|NNS>}                    
amount:    {<CD|DT>}
"""
cp = RegexpParser(gram)

text = "I'll have 1 pizza and 4 sandwiches"

def conversion(tree):
    dlist = []
    d = dict()
    for item in tree:
        if isinstance(item,Tree):
            d[item.label()] = ' '.join([l[0] for l in item.leaves()])
        else:
            dlist.append(d) if len(d)>0 else None
            d = dict()
    dlist.append(d) if len(d)>0 else None
    return dlist

parsed_text = [cp.parse(pos_tag(word_tokenize(sent)))
               for sent in sent_tokenize(text)]

for tree in parsed_text:
    print(conversion(tree))
#[{'amount': '1','plate': 'pizza'},{'amount': '4','plate': 'sandwiches'}]