如何在带有spacy的某个单词之后提取一个PERSON命名实体？

Question

我有这个文本（代码中的text2），它有3个'by'字，我想用Spacy提取人名（全名，即使是3个字，有些种族使用长名字，在这个案例2）。代码如下，我的模式显示错误。我的意图：首先用 ORTH 修复“by”词，然后告诉程序接下来的任何内容都是名为 PERSON 的词性实体。如果有人帮忙，我会很高兴：

import spacy
from spacy.matcher import Matcher
matcher = Matcher(nlp.vocab)
text2 = 'All is done by Emily Muller, the leaf is burned by fire. we were not happy, so we cut     relations by saying bye bye'
def extract_person(nlp_doc):
     pattern = [{'ORTH': 'by'}, {'POS': 'NOUN'}}]
     # second possible pattern:
     #pattern = [{"TEXT": "by"}, {"NER": "PERSON"}]
     matcher.add('person_only', None, pattern)
     matches = matcher(nlp_doc)
     for match_id, start, end in matches:
         span = nlp_doc[start:end]
         return span.text
target_doc = nlp(text2)
extract_person(target_doc)

我认为这个问题可以换个方式问：how to use NER tags in pattern in Matcher in spacy?

Answer 1

如果您想使用全名，您应该在开头合并实体。您可以拨打：nlp.add_pipe("merge_entities", after="ner")

然后在你的模式中而不是：

pattern = [{"TEXT": "by"}, {"NER": "PERSON"}]

用途：

pattern = [{"TEXT": "by"}, {"ENT_TYPE": "PERSON"}]

完整代码：

nlp.add_pipe("merge_entities", after="ner")

text2 = 'All is done by Emily Muller, the leaf is burned by fire. we were not happy, so we cut relations by saying bye bye'

doc = nlp(text2)

pattern = [{"TEXT": "by"}, {"ENT_TYPE": "PERSON"}]

matcher = Matcher(nlp.vocab)

matcher.add('person_only', [pattern])
matches = matcher(doc)
for match_id, start, end in matches:
    print(doc[start:end])