August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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146
｜
第
5
章
5.3.3
文档词条矩阵
我们将每个文档的行向量排列成一个矩阵，每一行表示一个文档，就可以得到文档
词条矩阵（
Document-Term Matrix
）。这个文档词条矩阵是所有文档的向量表征，
也是本书中所有机器学习任务的基础。在本章中，我们将使用它来计算文档的相似
性：
pd.DataFrame(onehot, columns=vocabulary)
输出结果：
注意：使用列表和数组表示文档词条矩阵的方法非常适合小型词汇表。对于大型词汇
表，我们需要找到一种更聪明的表示方式。
Scikit-learn
使用了
SciPy
（
https://oreil.ly/
yk1wx
）的稀疏向量和稀疏矩阵来处理这个问题。
计算相似性
为了计算文档之间的相似性，我们需要统计同一位置上均为
1
的元素个数。在
one-
hot
编码中，该操作非常快，只需要对各个向量进行“与”的位运算，然后统计结
果向量中
1
的个数。下面，我们来计算前两个句子的相似性：
sim = [onehot[0][i] & onehot[1][i] for i in range(0, len(vocabulary))]
sum(sim)
输出结果：
4
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