July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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6.6
提取词干
｜
103
讨论
尽管“停止词”可以指代所有需要在数据预处理阶段删除的单词，但是这个术语常常用
来指代那些特别常见而包含的信息又很少的单词。
NLTK
有一个常见停止词列表，可用
来查找并删除单词序列中的停止词 ：
#
查看停止词
stop_words[:5]
['i', 'me', 'my', 'myself', 'we']
注意，
NLTK
的
stopwords
假设所有的单词都是小写形式的。
6.6
提取词干
问题描述
将一个单词序列中的单词转换成它们的词干。
解决方案
使用
NLTK
的
PorterStemmer
：
#
加载库
from nltk.stem.porter import PorterStemmer
#
创建单词序列
tokenized_words = ['i', 'am', 'humbled', 'by', 'this', 'traditional',
'meeting']
#
创建词干转换器
porter = PorterStemmer()
#
应用词干转换器
[porter.stem(word) for word in tokenized_words]
['i', 'am', 'humbl', 'by', 'thi', 'tradit', 'meet']
讨论
词干提取（
stemming
）能识别出一个单词的词缀并将其删除（例如，动名词中的“
ing
”
后缀），同时保留其词根的意思，以此得到这个词的词干。例如，“
tradition
”和“
traditional
”
的词干都是“ ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.