将人称代词替换为前面提到的人(noise coref)

Question

我想做一个有噪声的解决方案，这样给定一个个人代词，该代词就会被前一个(最近的(人取代。

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.

输出为：

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：我已经使用NER提取了Person，但如何适当地替换代词？

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_)

Answer 1

有专门的neuralRef库来解决共引用。请参阅下面的最小可复制示例：

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.

注意，如果你pip安装neuralcoref，你可能会遇到一些问题，所以最好从源代码构建它，正如我在中概述的那样

Answer 2

我编写了一个函数，适用于您的两个示例：

考虑使用更大的模型(如en_core_web_lg(进行更准确的标记。

import spacy
from string import punctuation
nlp = spacy.load("en_core_web_lg")
def pronoun_coref(text):
doc = nlp(text)
pronouns = [(tok, tok.i) for tok in doc if (tok.tag_ == "PRP")]
names = [(ent.text, ent[0].i) for ent in doc.ents if ent.label_ == 'PERSON']
doc = [tok.text_with_ws for tok in doc]
for p in pronouns:
replace = max(filter(lambda x: x[1] < p[1], names),
key=lambda x: x[1], default=False)
if replace:
replace = replace[0]
if doc[p[1] - 1] in punctuation:
replace = ' ' + replace
if doc[p[1] + 1] not in punctuation:
replace = replace + ' '
doc[p[1]] = replace
doc = ''.join(doc)
return doc