Python 3.7中通过逐段计数单词的自定义数据结构

我有以下要求:

对于给定的单词或标记,确定它出现在多少段落中(称为文档频率)
创建一个数据结构(dict、pandas dataframe等),其中包含单词、其集合(总体)频率和文档频率

示例数据集如下所示:

<P ID=1>
I have always wanted to try like, multiple? Different rasteraunts. Not quite sure which kind, maybe burgers!
</P>

<P ID=2>
Nice! I love burgers. Cheeseburgers, too. Have you ever gone to a diner type restauraunt? I have always wanted to try every diner in the country.
</P>

<P ID=3>
I am not related to the rest of these paragraphs at all.
</P>

一个“段落”是由 <P ID=x> </P> tags

我需要的是创建一个类似这样的数据结构(我认为它是 dict ):

{'i': X Y, 'have': X Y, etc}

或者,可能是 pandas 如下所示的数据帧:

| Word | Content Frequency | Document Frequency |
|   i  |         4         |          3         |
| have |         3         |          2         |
| etc  |         etc       |          etc       |

目前,我可以找到内容频率没有问题使用下面的代码。

import nltk
import string
from nltk.tokenize import word_tokenize, RegexpTokenizer
import csv
import numpy
import operator
import re

# Requisite
def get_input(filepath):
    f = open(filepath, 'r')
    content = f.read()
    return content

# 1
def normalize_text(file):
    file = re.sub('<P ID=(\d+)>', '', file)
    file = re.sub('</P>', '', file)
    tokenizer = RegexpTokenizer(r'\w+')
    all_words = tokenizer.tokenize(file)
    lower_case = []
    for word in all_words:
        curr = word.lower()
        lower_case.append(curr)

    return lower_case

# Requisite for 3
# Answer for 4
def get_collection_frequency(a):
    g = {}
    for i in a:
        if i in g: 
            g[i] +=1
        else: 
            g[i] =1
    return g

myfile = get_input('example.txt')
words = normalize_text(myfile)

## ANSWERS
collection_frequency = get_collection_frequency(words)
print("Collection frequency: ", collection_frequency)

Collection frequency:  {'i': 4, 'have': 3, 'always': 2, 'wanted': 2, 
'to': 4, 'try': 2, 'like': 1, 'multiple': 1, 'different': 1,
'rasteraunts': 1, 'not': 2, 'quite': 1, 'sure': 1, 'which': 1,
'kind': 1, 'maybe': 1, 'burgers': 2, 'nice': 1, 'love': 1,
'cheeseburgers': 1, 'too': 1, 'you': 1, 'ever': 1, 'gone': 1, 'a': 1,
'diner': 2, 'type': 1, 'restauraunt': 1, 'every': 1, 'in': 1, 'the': 2,
'country': 1, 'am': 1, 'related': 1, 'rest': 1, 'of': 1, 'these': 1, 
'paragraphs': 1, 'at': 1, 'all': 1}

但是,我目前正在删除 normalize_text 功能与行:

file = re.sub('<P ID=(\d+)>', '', file)
file = re.sub('</P>', '', file)

P , ID , 1 , 2 , 3 在我的字典里算一下,因为那些只是段落标题。

那么,我怎样才能将一个单词的出现与它在一个段落中的实例联系起来,从而产生上面所期望的结果呢?我甚至不确定试图创建这样一个数据结构的逻辑。