August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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通过单词嵌入探索语义关系
｜
323
我们使用了
Gensim
来训练、存储和分析我们的嵌入。
Gensim
非常受欢迎，但你
也可以尝试其他更快的方法，比如（
Py
）
Magnitude
（
https://oreil.ly/UlRzX
）或
finalfusion
（
https://oreil.ly/TwM4h
）。当然，你也可以使用
TensorFlow
和
PyTorch
训练不同种类的嵌入。
如今，语义嵌入是所有复杂的机器学习任务的关键。然而，对于情感分析或段落检
测之类的任务，你不需要单词嵌入，你需要的是句子或整个文档的嵌入。关于创建
文档嵌入，已经发表了很多不同的方法（
Wolf
，
2018
年；
Palachy
，
2019
年）。一
种常见的方法是计算句子中单词向量的平均值。一些
spaCy
模型的词汇表中包含单
词向量（
https://oreil.ly/zI1wm
），而且允许你利用现成的平均单词向量来计算文档
相似度。但是，平均单词向量仅适用单个句子或非常短的文档。此外，该方法受限
于词袋模型，无法考虑单词的顺序。
最新的模型同时利用了语义嵌入与词序的功能。在下一章中，我们将使用这种模型
处理情感分类。
10.7
补充阅读
Bolukbasi
、
Tolga
、
Kai-Wei Chang
、
James Zou
、
Venkatesh Saligrama
与
Adam
Kalai.
“
Man Is to Computer Programmer as Woman Is to Homemaker? Debiasing
Word Embeddings ...
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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.