在为特定的极端情况正确识别文本中的句子时，我有些麻烦：
"。到目前为止，这是我识别文本中句子的方式（来源：Subtitles Reformat to end with complete sentence）：
re.findall部分基本上是查找str的一部分，该部分以大写字母[A-Z]开头，然后是除标点符号之外的所有内容，然后以标点符号[\.?!]结尾
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.
案例1：点，点，点
不保留点，点，点，因为如果连续出现三个点，则没有给出如何处理的说明。如何更改？
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.
案例2：”
"符号已成功保留在句子中，但是像标点后面的点一样，它会在末尾删除。
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.
案例3：小写句子的开头
如果一个句子意外地以小写开头，则该句子将被忽略。目的是确定先前的句子已结束（或文本刚开始），因此必须开始新的句子。
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.
非常感谢您的帮助！
编辑：
我测试过：
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]
...但是我得到了
--------------------------------------------------------------------------- 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'））或者，添加 依赖分析器，或通过设置来设置句子边界 doc [i] .is_sent_start。
答案 0 :(得分：2)
您可以为此使用一些工业包装。例如，spacy具有非常好的句子标记器。
from __future__ import unicode_literals, print_function
from spacy.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]
您的方案：
案例结果-> ['We were able to respond to the first research question...', 'Next, we also determined the size of the population.']
案例结果-> ['We were able to respond to the first "research" question: "What is this?"', 'Next, we also determined the size of the population.']
案例结果-> ['We were able to respond to the first research question.', 'next, we also determined the size of the population.']
答案 1 :(得分：2)
您可以修改正则表达式以匹配您的特殊情况。
首先，您不需要在.内转义[]
对于第一个极端情况，您可以用[.!?]*贪婪地匹配end-ancetance-token。
第二次，您可以匹配"之后的[.!?]
对于最后一个，您可以从上或下开始：
import re
regex = r'([A-z][^.!?]*[.!?]*"?)'
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(regex, text):
print(sentence)
print()
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(regex, text):
print(sentence)
print()
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(regex, text):
print(sentence)
[A-z]，每次比赛都应以大写或小写字母开头。[^.?!]*，它贪婪地匹配不是.，?或!（结束情感字符）的任何字符[.?!]*，它会贪婪地匹配结尾字符，因此...??!!???将作为情感部分进行匹配"?，它最终与句子结尾处的报价匹配情况一：
我们能够回答第一个研究问题... 接下来，我们还确定了人口规模。
情况2：
我们能够回答第一个“研究”问题：“这是什么？” 接下来，我们还确定了人口规模。
情况3：
我们能够回答第一个研究问题。 接下来，我们还确定了人口规模。
答案 2 :(得分：1)
您可以使用nltk sent_tokenize。这样可以避免很多麻烦。
from nltk import sent_tokenize
# Corner Case 1: Dot, Dot, Dot
text_dot_dot_dot = "We were able to respond to the first research question... Next, we also determined the size of the population."
print("Corner Case 1: ", sent_tokenize(text_dot_dot_dot))
# Corner Case 1: "
text_ = "We were able to respond to the first \"research\" question: \"What is this?\" Next, we also determined the size of the population."
print("Corner Case 2: ", sent_tokenize(text_))
# Corner Case 1: lower case
text_lower = "We were able to respond to the first research question. next, we also determined the size of the population."
print("Corner Case 2: ", sent_tokenize(text_lower))
结果：
Corner Case 1: ['We were able to respond to the first research question... Next, we also determined the size of the population.']
Corner Case 2: ['We were able to respond to the first "research" question: "What is this?"', 'Next, we also determined the size of the population.']
Corner Case 2: ['We were able to respond to the first research question.', 'next, we also determined the size of the population.']
答案 3 :(得分：0)
尝试以下正则表达式： （[A-Z] [^。！？] * [。！？] + [“]？）
'+'表示一个或多个
'？'表示零或更多
这应该通过您上面提到的所有3个极端情况