如何在结果中提取chunker？

Question

我试图提取文本文件中每个句子的动词和名词（以其词干形式）包含1000个问题。我想计算所有问题中动词和名词的整体频率（出现次数/问题数）。查找所有常用动词和名词，使其总体频率大于阈值（例如，0.2，使其成为参数）

所以输出就是 Q1：频繁的verb1，frequency1;频繁的动词2，频率2; ...;频繁名词1，频率1，....

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize, RegexpTokenizer
from nltk.stem import PorterStemmer

with open('test.txt', encoding='utf8',) as Test_file:
     text = Test_file.read()


def stop_words(a):
     stop_words= set(stopwords.words('english'))
     important_words = []
     for w in a:
         if w.lower() not in stop_words:
             important_words.append(w.lower())
     return important_words

def take_words(a):
    sentences = nltk.sent_tokenize(a) 
    sentences = [nltk.word_tokenize(sent) for sent in sentences]
    sentences = [nltk.pos_tag(sent) for sent in sentences]
    return sentences


def stem_words(a):
    words_stem= []
    PS = PorterStemmer()
    for r in a:
        words_stem.append(PS.stem(r).lower())
    return words_stem


def chunking(a):
    Grammar = r"""
    NN:       {<NN.*>+}
    VB:       {<VB.*>+}          
    """
    ChunkParser = nltk.RegexpParser(Grammar)
    for w in a:
        Chunkit = ChunkParser.parse(w)

现在我不知道下一步怎么办？有人能给我一些帮助吗？