【发布时间】:2019-11-03 18:57:24
【问题描述】:
考虑以下句子:
“敏捷的棕狐跳过懒狗”
如果我想提取棕色,只提取棕色相当容易,但是我想要以下输出:
"brown" , "敏捷的棕色狐狸跳过懒狗"
【问题讨论】:
标签: python spacy named-entity-recognition
考虑以下句子:
“敏捷的棕狐跳过懒狗”
如果我想提取棕色,只提取棕色相当容易,但是我想要以下输出:
"brown" , "敏捷的棕色狐狸跳过懒狗"
【问题讨论】:
标签: python spacy named-entity-recognition
是的,在 Spacy 中有一种方法可以做到这一点。您必须遍历实体 Span 对象并从每个 Span 对象中提取句子。这是一个例子:
doc = nlp("John and Claire live in London. They have a dog. Claire walks her
dog everyday.")
for entity in doc.ents:
print('Entity extracted : ', entity.text)
print('Sentence extracted from : ', entity.sent)
这应该会给你以下输出:
Entity extracted : John
Sentence extracted from : John and Claire live in London.
Entity extracted : Claire
Sentence extracted from : John and Claire live in London.
Entity extracted : London
Sentence extracted from : John and Claire live in London.
Entity extracted : Claire
Sentence extracted from : Claire walks her dog everyday.
Entity extracted : everyday
Sentence extracted from : Claire walks her dog everyday.
【讨论】: