Jes*_*ABI 4 python nlp python-3.x spacy coreference-resolution
我想做一个嘈杂的解决方案,以便给定一个人称代词,该代词被前一个(最近的)人代替。
例如:
Alex is looking at buying a U.K. startup for $1 billion. He is very confident that this is going to happen. Sussan is also in the same situation. However, she has lost hope.
输出是:
Alex is looking at buying a U.K. startup for $1 billion. Alex is very confident that this is going to happen. Sussan is also in the same situation. However, Susan has lost hope.
另一个例子,
Peter is a friend of Gates. But Gates does not like him.
在这种情况下,输出将是:
Peter is a friend of Gates. But Gates does not like Gates.
是的!这太吵了。
使用spacy:我已经提取了Personusing NER,但是如何正确替换代词?
代码:
import spacy
nlp = spacy.load("en_core_web_sm")
for ent in doc.ents:
if ent.label_ == 'PERSON':
print(ent.text, ent.label_)
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有专门的neuralcoref库来解决共指问题。请参阅下面的最小可重现示例:
import spacy
import neuralcoref
nlp = spacy.load('en_core_web_sm')
neuralcoref.add_to_pipe(nlp)
doc = nlp(
'''Alex is looking at buying a U.K. startup for $1 billion.
He is very confident that this is going to happen.
Sussan is also in the same situation.
However, she has lost hope.
Peter is a friend of Gates. But Gates does not like him.
''')
print(doc._.coref_resolved)
Alex is looking at buying a U.K. startup for $1 billion.
Alex is very confident that this is going to happen.
Sussan is also in the same situation.
However, Sussan has lost hope.
Peter is a friend of Gates. But Gates does not like Peter.
Run Code Online (Sandbox Code Playgroud)
请注意,如果您 pip 安装它,您可能会遇到一些问题neuralcoref,因此最好从源代码构建它,正如我在此处概述的那样