spacy中的重要名称实体识别

Question

我正在尝试为一个句子找到重要的命名实体，如下所示：

import spacy

nlp = spacy.load('en')

sentences = "The machine learning is a field within computer science,\
it differs from traditional computational approaches. In traditional computing,\
algorithms are sets of explicitly programmed instructions used by computers to \
calculate or problem solve. The Machine learning algorithms instead allow for computers \
to train on data inputs and use statistical analysis in order to output values that fall\
within a specific range. Because of this, machine learning facilitates computers in building\
models from sample data in order to automate decision-making processes based on data inputs."

doc = nlp(sentences)

print('Name Entity:{0}'.format(doc.ents))

我期望得到“机器学习”，“算法”，“决策”的结果，但我得到的结果是一个空集。我到底做错了什么。

Answer 1

spacy en model只给你提供像名字，地点，日期，ORG等自然实体。如果你想要一些自定义实体标签，那么你已经创建了你自己的带有训练的自定义模型。有关自定义模型创建的更多信息，请访问My another post.

Answer 2

这些句子中没有实体。

试一试

import spacy

nlp = spacy.load('en_core_web_sm')

sentences = "The machine learning is a field within computer science,\
it differs from traditional computational approaches. In traditional computing,\
algorithms are sets of explicitly programmed instructions used by computers to \
calculate or problem solve. The achine learning algorithms instead allow for computers \
to train on data inputs and use statistical analysis in order to output values that fall\
within a specific range. Because of this, at Apple, machine learning facilitates computers in building\
models from sample data in order to automate decision-making processes based on data inputs."

doc = nlp(sentences)

print('Name Entity:{0}'.format(doc.ents))

Answer 3

当我运行代码时，这是我收到的输出

​

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对于像“机器学习”，“算法”这样的输出，你需要定制spacy NER并进行相应的训练，.You可以为它使用正则表达式。