January 2018
Intermediate to advanced
301 pages
8h 54m
Chinese
Content preview from Python机器学习基础教程
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254
｜
第
7
章
我们要解决的任务如下：给定一条评论，我们希望根据该评论的文本内容对其分配一个
“正面的”或“负面的”标签。这是一项标准的二分类任务。但是，文本数据并不是机器
学习模型可以处理的格式。我们需要将文本的字符串表示转换为数值表示，从而可以对其
应用机器学习算法。
7.3
将文本数据表示为词袋
用于机器学习的文本表示有一种最简单的方法，也是最有效且最常用的方法，就是使用
词
袋
（
bag-of-words
）表示。使用这种表示方式时，我们舍弃了输入文本中的大部分结构，如
章节、段落、句子和格式，只计算语料库中
每个单词在每个文本中的出现频次
。舍弃结构
并仅计算单词出现次数，这会让脑海中出现将文本表示为“袋”的画面。
对于文档语料库，计算词袋表示包括以下三个步骤。
(1)
分词
（
tokenization
）。将每个文档划分为出现在其中的单词
[
称为词例（
token
）
]
，比如
按空格和标点划分。
(2)
构建词表（
vocabulary building
）。收集一个词表，里面包含出现在任意文档中的所有词，
并对它们进行编号（比如按字母顺序排序）。
(3)
编码
（
encoding
）。对于每个文档，计算词表中每个单词在该文档中的出现频次。
在步骤
1
和步骤
2
中涉及一些细微之处，我们将在本章后面进一步深入讨论。目前，我们
来看一下如何利用 scikit-learn 来应用词袋处理过程。图
7-1
展示了对字符串 "This is
how you get ants." 的处理过程。其输出是包含每个文档中单词计数的一个向量。对于词 ...
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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.