Python机器学习NLP自然语言处理基本操作词袋模型

import jieba
from gensim import corpora
# 定义标点符号
punctuation = ["，", "。", "：", "；", "?", "!"]
# 定义语料
content = [
 "今天天气真不错!",
 "明天要下雨?",
 "后天要打雷。"
]
# 分词
seg = [jieba.lcut(con) for con in content]
print("语料:", seg)
# 去除标点符号
tokenized = seg.copy()
for s in tokenized:
 for p in punctuation:
  if p in s:
s.remove(p)
print("去除标点:", tokenized)
# tokenized是去标点之后的
dictionary = corpora.Dictionary(seg)
print("词袋模型:", dictionary)
# 保存词典
dictionary.save('deerwester.dict')
# 查看字典和下标id的映射
print("编号:", dictionary.token2id)

Building prefix dict from the default dictionary ...
Loading model from cache C:\Users\Windows\AppData\Local\Temp\jieba.cache
Loading model cost 1.140 seconds.
Prefix dict has been built successfully.
语料: [['今天天气', '真不错', '!'], ['明天', '要', '下雨', '?'], ['后天', '要', '打雷', '。']]
去除标点: [['今天天气', '真不错'], ['明天', '要', '下雨'], ['后天', '要', '打雷']]
词袋模型: Dictionary(7 unique tokens: ['今天天气', '真不错', '下雨', '明天', '要']...)
编号: {'今天天气': 0, '真不错': 1, '下雨': 2, '明天': 3, '要': 4, '后天': 5, '打雷': 6}