我在正确识别文本中特定角落情况下的句子时遇到了一点麻烦：
1.如果涉及一个点，这将不会被保留。
1.如果涉及"。
1.如果一个句子不小心以小写字母开头。
这就是我到目前为止在文本中识别句子的方法（来源：字幕Reformat to end with complete sentence）：
re.findall部分基本上是查找一个以大写字母[A-Z]开头的str块，然后是除标点符号之外的任何内容，然后以标点符号[\.?!]结束。

import re
text = "We were able to respond to the first research question. Next, we also determined the size of the population."
    for sentence in re.findall(r'([A-Z][^\.!?]*[\.!?])', text):
        print(sentence + "\n")

We were able to respond to the first research question.

Next, we also determined the size of the population.

角落案例1：点点点点
点，不被保留，因为没有指示如果三个点出现在一行中该怎么做。如何才能改变这种状况？

text = "We were able to respond to the first research question... Next, we also determined the size of the population."
for sentence in re.findall(r'([A-Z][^\.!?]*[\.!?])', text):
    print(sentence + "\n")

We were able to respond to the first research question.

Next, we also determined the size of the population.

角落案例2："
"符号成功地保留在句子中，但就像标点符号后面的点一样，它将在结尾被删除。

text = "We were able to respond to the first \"research\" question: \"What is this?\" Next, we also determined the size of the population."
for sentence in re.findall(r'([A-Z][^\.!?]*[\.!?])', text):
    print(sentence + "\n")

We were able to respond to the first "research" question: "What is this?

Next, we also determined the size of the population.

角落案例3：句子开头小写
如果一个句子意外地以小写字母开头，该句子将被忽略。其目的是确定前一句已经结束（或文本刚刚开始），因此必须开始一个新的句子。

text = "We were able to respond to the first research question. next, we also determined the size of the population."
for sentence in re.findall(r'([A-Z][^\.!?]*[\.!?])', text):
    print(sentence + "\n")

We were able to respond to the first research question.

编辑

我测试了一下：

import spacy
from spacy.lang.en import English

raw_text = 'Hello, world. Here are two sentences.'
nlp = English()
doc = nlp(raw_text)
sentences = [sent.string.strip() for sent in doc.sents]

但我得到的是

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-157-4fd093d3402b> in <module>()
      6 nlp = English()
      7 doc = nlp(raw_text)
----> 8 sentences = [sent.string.strip() for sent in doc.sents]

<ipython-input-157-4fd093d3402b> in <listcomp>(.0)
      6 nlp = English()
      7 doc = nlp(raw_text)
----> 8 sentences = [sent.string.strip() for sent in doc.sents]

doc.pyx in sents()

ValueError: [E030] Sentence boundaries unset. You can add the 'sentencizer' component to the pipeline with:
nlp.add_pipe(nlp.create_pipe('sentencizer')) Alternatively, add the
dependency parser, or set sentence boundaries by setting
doc[i].is_sent_start.