August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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295
第 10 章
通过单词嵌入探索语义关系
相似性的概念是所有机器学习任务的基础。在第
5
章中，我们介绍了如何根据词袋
模型计算文本的相似性。根据两个文档的
TF-IDF
向量计算余弦相似性非常简单，
而且我们可以利用这个信息来搜索、聚类或分类相似的文档。
然而，词袋模型中的相似性概念完全基于两个文档中共有的单词数量。如果文档之
间没有共享任何单词，则文档向量的点积，也就是余弦相似性为
0
。考虑下面两则
有关某个新电影的评论（来自某个社交平台）：
“
What a wonderful movie.
”
“
The film is great
.”
很显然，尽管这两个评论使用的单词完全不同，但它们的意思非常相似。在本章中，
我们将介绍单词嵌入，这种方式能够捕捉到单词的语义，并利用它们探索语料库中
的语义相似性。
10.1
本章内容概要
假设我们都是市场研究人员，希望通过有关车辆的文本，更好地理解车辆市场中的
一些关系。具体来讲，我们想探索车辆品牌与车型之间的相似性。例如，品牌
A
中
的哪个车型与品牌
B
的某个车型最为相似？
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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.