August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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356
｜
第
12
章
目标是将文本中的指称链接到本体中唯一的真实实体。如此一来，就可以消除模糊
性：
Q64
指的是德国柏林，而不是新罕布什尔州的柏林（这个柏林在维基数据中是
Q821244
）。在连接不同资源的信息并构建知识库时，这一步非常关键。
命名
实体识别
共指消解 实体链接
关系提取
信息提取
清理文本
知识图谱
图 12-2：信息提取的过程
最后一步是关系提取（
relation extraction
），即识别实体之间的关系。在实际的应用中，
通常你只会考虑几个关系，因为很难从任意文本中正确抽取这类信息。
最后，你可以将这个图保存到图数据库中，作为知识库应用程序的后台。这样的图
数据库存储的数据可以是
RDF
三元组（三元存储），也可以是属性图的形式，其中
节点和边可以拥有任意属性。常用的图数据库有
GraphDB
（三元存储）、
Neo4j
和
Grakn
（属性图）。
对于每一个步骤，你都可以从基于规则的方式和机器学习中进行选择。我们将使用
spaCy
提供的模型，以及一些额外的规则。但是我们并不会训练自己的模型。使用
规则提取领域特定的知识有一个优势：无需训练数据即可快速着手。我们可以看到，
使用基于规则的方法产生的结果，也能进行一些非常有意义的分析。但是如果你打
算构建一个大规模的预料知识库，那么必须训练自己的模型，识别命名实体，检测
关系，并链接实体。
12.3
数据集简介
假设你从事的是金融业务，你希望跟进并购与收购方面的新闻。我们希望能够自动
识别公司名称以及他们的交易类型，并将结果放入知识库中。在本章中，我们将
介绍提取有关公司的某些信息的方法 ...
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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.