August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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知识图谱
｜
363
这个函数接受一个
spaCy
的
Doc
对象（名叫
doc
）作为参数，并返回一个
Doc
。因此，
我们可以将它作为流水线的一个组件，添加到已有的流水线中：
nlp.add_pipe(norm_entities)
现在，我们针对各个示例的句子，重复这个过程，并检查结果：
doc = nlp(text)
print(*[([t.text for t in e], e.label_) for e in doc.ents], sep='
\n
')
输出结果：
(['Baker', 'International'], 'ORG')
(['New', 'York', 'Stock', 'Exchange'], 'ORG')
12.4.3
合并实体词语
在很多情况下，我们应该将合成的名称（如上例所示）当成一个词语，因为这样可
以简化句子的结构。为此，
spaCy
提供了一个内置的流水线函数
merge_entities
。
我们将它添加到我们的
NLP
流水线中，将每个命名实体都合并成一个词语：
from spacy.pipeline import merge_entities
nlp.add_pipe(merge_entities)
doc = nlp(text)
print(*[(t.text, t.ent_type_) for t in doc if t.ent_type_ != ''])
输出结果：
('Baker International', 'ORG') ('New York Stock Exchange', 'ORG') ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.