将人称代词替换为之前提到的人称（吵闹的coref）

Question

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_)

Answer 1

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.
 

注意，如果您使用 pip 安装它，您可能会遇到一些问题，因此最好从源代码构建它，正如我在此处概述的那样

Answer 2

考虑使用更大的模型，例如

neuralcoref

以获得更准确的标记。

en_core_web_lg

Answer 3

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