August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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准备统计和机器学习的文本数据
｜
131
一般，我们建议使用词元，不要使用变形后的单词，但这不是绝对的。
例如，在情感分析中，“
good
”和“
best
”表达了不同的含义，因此使
用词元可能会产生负面影响。
4.6.6
案例：提取名词短语
在第
1
章中，我们介绍了如何在文本分析中使用
n
元语法。
n
元语法是句
子中连续的
n
个单词。例如，我们之前使用的句子包含以下二元语法：
My_best|best_friend|friend_Ryan|Ryan_Peters|Peters_likes|likes_fancy
fancy_adventure|adventure_games
上述许多二元语法都对分析没有帮助，例如“
likes_fancy
”或“
my_best
”。三元
语法的情况可能更糟。但是，我们如何才能检测到具有真正含义的单词序列呢？一
种方法是通过模式匹配过滤词性标签。
spaCy
拥有一个非常强大的基于规则的匹配
器（
https://oreil.ly/VjOJK
）
，而
textacy
则提供了一个非常便利的封装函数，可以实
现基于模式的短语提取（
https://oreil.ly/Xz70U
）。下列模式能够提取前面带有形容
词的名词序列：
text = "My best friend Ryan Peters likes fancy adventure games."
doc = nlp(text)
patterns = ["POS:ADJ POS:NOUN:+"]
spans = textacy.extract.matches(doc, ...
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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.