August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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274
｜
第
9
章
important parts of the company to other buyers,...
officials and analysts have said.
在这种方法中，我们使用
TF-IDF
值创建了文本的中间表示，并根据这个表示给各
个句子打分，最后挑选出分数最高的三个句子。使用这种方法选择的句子与前面
我们手工书写的相吻合，抓住了文章的要点。虽然有一些细微的差别，比如高通
（
Qualcomm
）在该产业中的重要性，以及
5G
科技的具体应用等内容都丢失了，但
是这种方法可以作为一种良好的解决方案，快速识别新闻文章中的重要句子，并自
动生成摘要。最后，我们提一下本书附带的
notebook
中定义的
tfidf_summary
函数，
本章后面会用到它。
9.3.2 LSA
算法
潜在语义分析（
Latent Semantic Analysis
，简称
LSA
）是提取式摘要中常用的现代
算法之一。
LSA
是一种通用的方法，可用于主题建模、文档相似性分析以及其他任
务。
LSA
假定意思相近的单词会出现在同一个文档内。在
LSA
算法中，我们首先
以句子词条矩阵的形式表示整个文章。文档词条矩阵的概念已经在第
8
章中介绍过
了，我们改变一下这个概念，变成句子词条矩阵。每一行表示一个句子，而每一列
表示一个单词。该矩阵中每个元素的值是词频，通常会按照
TF-IDF
权重调整比例。
这种方法的目标是通过创建改版的句子词条矩阵表示，将所有单词删减到几个主题。
为了创建改版表示，我们需要应用非负矩阵分解方法，将这个矩阵表示成两个分解 ...
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