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Python文本分析
book

Python文本分析

by Jens Albrecht, Sidharth Ramachandran, Christian Winkler
August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
China Electric Power Press Ltd.
Content preview from Python文本分析
122
4
4.6.1
实例化流水线
下面,我们来学习使用
spaCy
。第一步,我们需要实例化
spaCy
Language
类的对象,
为此需要调
spacy.load()
并指定将要使用的模型文件名。
6
在本章中,我们
将使用小型英语模型
en_core_web_sm
Language
对象的变量通常会命名为
nlp
import spacy
nlp = spacy.load('en_core_web_sm')
现在,这个
Language
对象中包含共享词汇表、模型以及处理流水线。你可以通过对
象的以下属性检查流水线组件:
nlp.pipeline
输出结果:
[('tagger', <spacy.pipeline.pipes.Tagger at 0x7fbd766f84c0>),
('parser', <spacy.pipeline.pipes.DependencyParser at 0x7fbd813184c0>),
('ner', <spacy.pipeline.pipes.EntityRecognizer at 0x7fbd81318400>)]
默认的流水线由词性标注器(
tagger
)、解析器(
parser
)和命名实体识别器
ner
组成,所有这些都需要根据特定的语言选择。这里没有明确列出分词器,是因为分
词器在任何情况下都是必需的。
spaCy
的分词器非常快,但是所有其他步骤都基于神经模型,并且需要花费大量时间。
但是与其他库相比,
spaCy
的模型是最快的。处理整个流水线所需的时间仅是分词
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Publisher Resources

ISBN: 9787519864446