blocks|key|958758|text|您可以检查DocumentPreprocessor类。下面是一个简短的代码片段。我认为可能还有其他方法可以做你想做的事情。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|958759|String+paragraph+=+"My+1st+sentence.+“Does+it+work+for+questions?”+My+third+sentence.";
Reader+reader+=+new+StringReader(paragraph);
DocumentPreprocessor+dp+=+new+DocumentPreprocessor(reader);
List<String>+sentenceList+=+new+ArrayList<String>();

for+(List<HasWord>+sentence+:+dp)+{
+++//+SentenceUtils+not+Sentence
+++String+sentenceString+=+SentenceUtils.listToString(sentence);
+++sentenceList.add(sentenceString);
}

for+(String+sentence+:+sentenceList)+{
+++System.out.println(sentence);
}|code-block|syntax|javascript|958760|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

You can check the DocumentPreprocessor class. Below is a short snippet. I think there may be other ways to do what you want.

<pre><code>String paragraph = "My 1st sentence. “Does it work for questions?” My third sentence.";
Reader reader = new StringReader(paragraph);
DocumentPreprocessor dp = new DocumentPreprocessor(reader);
List&lt;String&gt; sentenceList = new ArrayList&lt;String&gt;();

for (List&lt;HasWord&gt; sentence : dp) {
 // SentenceUtils not Sentence
 String sentenceString = SentenceUtils.listToString(sentence);
 sentenceList.add(sentenceString);
}

for (String sentence : sentenceList) {
 System.out.println(sentence);
}
</code></pre>

blocks|key|711140|text|我知道已经有一个被接受的answer...but，通常您只需要从带注释的文档中获取SentenceAnnotations即可。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|711141|//+creates+a+StanfordCoreNLP+object,+with+POS+tagging,+lemmatization,+NER,+parsing,+and+coreference+resolution+
Properties+props+=+new+Properties();
props.put("annotators",+"tokenize,+ssplit,+pos,+lemma,+ner,+parse,+dcoref");
StanfordCoreNLP+pipeline+=+new+StanfordCoreNLP(props);

//+read+some+text+in+the+text+variable
String+text+=+...+//+Add+your+text+here!

//+create+an+empty+Annotation+just+with+the+given+text
Annotation+document+=+new+Annotation(text);

//+run+all+Annotators+on+this+text
pipeline.annotate(document);

//+these+are+all+the+sentences+in+this+document
//+a+CoreMap+is+essentially+a+Map+that+uses+class+objects+as+keys+and+has+values+with+custom+types
List<CoreMap>+sentences+=+document.get(SentencesAnnotation.class);

for(CoreMap+sentence:+sentences)+{
++//+traversing+the+words+in+the+current+sentence
++//+a+CoreLabel+is+a+CoreMap+with+additional+token-specific+methods
++for+(CoreLabel+token:+sentence.get(TokensAnnotation.class))+{
++++//+this+is+the+text+of+the+token
++++String+word+=+token.get(TextAnnotation.class);
++++//+this+is+the+POS+tag+of+the+token
++++String+pos+=+token.get(PartOfSpeechAnnotation.class);
++++//+this+is+the+NER+label+of+the+token
++++String+ne+=+token.get(NamedEntityTagAnnotation.class);+++++++
++}

}|code-block|syntax|javascript|711142|Source+-+http://nlp.stanford.edu/software/corenlp.shtml+(减半)|offset|length|711143|如果你只是在寻找句子，你可以从流水线初始化中去掉后面的步骤，比如"parse“和"dcoref”，这将为你节省一些负载和处理时间。摇滚乐。~K|711144|entityMap|0|LINK|mutability|MUTABLE|url|http://nlp.stanford.edu/software/corenlp.shtml^0|0|0|9|1A|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|W|8|@]|9|@$I|X|J|Y|1|Z]]|A|$]]|$1|K|3|L|5|6|7|10|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|T]]]]

I know there is already an accepted answer...but typically you'd just grab the SentenceAnnotations from an annotated doc.

<pre><code>// creates a StanfordCoreNLP object, with POS tagging, lemmatization, NER, parsing, and coreference resolution 
Properties props = new Properties();
props.put("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);

// read some text in the text variable
String text = ... // Add your text here!

// create an empty Annotation just with the given text
Annotation document = new Annotation(text);

// run all Annotators on this text
pipeline.annotate(document);

// these are all the sentences in this document
// a CoreMap is essentially a Map that uses class objects as keys and has values with custom types
List&lt;CoreMap&gt; sentences = document.get(SentencesAnnotation.class);

for(CoreMap sentence: sentences) {
 // traversing the words in the current sentence
 // a CoreLabel is a CoreMap with additional token-specific methods
 for (CoreLabel token: sentence.get(TokensAnnotation.class)) {
 // this is the text of the token
 String word = token.get(TextAnnotation.class);
 // this is the POS tag of the token
 String pos = token.get(PartOfSpeechAnnotation.class);
 // this is the NER label of the token
 String ne = token.get(NamedEntityTagAnnotation.class); 
 }

}
</code></pre>

Source - <a href="http://nlp.stanford.edu/software/corenlp.shtml">http://nlp.stanford.edu/software/corenlp.shtml</a> (half way down)

And if you're only looking for sentences, you can drop the later steps like "parse" and "dcoref" from the pipeline initialization, it'll save you some load and processing time. Rock and roll.
~K

blocks|key|958861|text|使用.net+C#包:这将拆分句子，使括号正确，并保留原始空格和标点符号：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|958862|public+class+NlpDemo
{
++++public+static+readonly+TokenizerFactory+TokenizerFactory+=+PTBTokenizer.factory(new+CoreLabelTokenFactory(),
++++++++++++++++"normalizeParentheses=false,normalizeOtherBrackets=false,invertible=true");

++++public+void+ParseFile(string+fileName)
++++{
++++++++using+(var+stream+=+File.OpenRead(fileName))
++++++++{
++++++++++++SplitSentences(stream);
++++++++}
++++}

++++public+void+SplitSentences(Stream+stream)
++++{++++++++++++
++++++++var+preProcessor+=+new+DocumentPreprocessor(new+UTF8Reader(new+InputStreamWrapper(stream)));
++++++++preProcessor.setTokenizerFactory(TokenizerFactory);

++++++++foreach+(java.util.List+sentence+in+preProcessor)
++++++++{
++++++++++++ProcessSentence(sentence);
++++++++}++++++++++++
++++}

++++//+print+the+sentence+with+original+spaces+and+punctuation.
++++public+void+ProcessSentence(java.util.List+sentence)
++++{
++++++++System.Console.WriteLine(edu.stanford.nlp.util.StringUtils.joinWithOriginalWhiteSpace(sentence));
++++}
}|code-block|syntax|javascript|958863|Input:+-这个句子的字符具有某种魅力，这种魅力经常出现在标点符号和散文中。这是第二句话吗？确实是这样。|offset|length|style|BOLD|958864|输出：3个句子('?‘被认为是句末分隔符)|958865|注:对于像“Havisham夫人的课程在所有方面都是无可挑剔的(就人们所能看到的！)”这样的句子。标记器将正确地识别出Mrs.末尾的句点不是EOS，但是它将错误地标记！在括号内作为EOS和拆分“在所有方面”。作为第二句话。|958866|entityMap^0|0|0|0|6|0|0|3|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@$I|V|J|W|K|L]]|9|@]|A|$]]|$1|M|3|N|5|6|7|X|8|@$I|Y|J|Z|K|L]]|9|@]|A|$]]|$1|O|3|P|5|6|7|10|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|11|8|@]|9|@]|A|$]]]|R|$]]

Using the .net C# package:
This will split sentences, get the parentheses correct and preserve original spaces and punctuation:

<pre><code>public class NlpDemo
{
 public static readonly TokenizerFactory TokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(),
 "normalizeParentheses=false,normalizeOtherBrackets=false,invertible=true");

 public void ParseFile(string fileName)
 {
 using (var stream = File.OpenRead(fileName))
 {
 SplitSentences(stream);
 }
 }

 public void SplitSentences(Stream stream)
 { 
 var preProcessor = new DocumentPreprocessor(new UTF8Reader(new InputStreamWrapper(stream)));
 preProcessor.setTokenizerFactory(TokenizerFactory);

 foreach (java.util.List sentence in preProcessor)
 {
 ProcessSentence(sentence);
 } 
 }

 // print the sentence with original spaces and punctuation.
 public void ProcessSentence(java.util.List sentence)
 {
 System.Console.WriteLine(edu.stanford.nlp.util.StringUtils.joinWithOriginalWhiteSpace(sentence));
 }
}
</code></pre>

Input:
 - This sentence's characters possess a certain charm, one often found in punctuation and prose. This is a second sentence? It is indeed.

Output:
3 sentences ('?' is considered an end-of-sentence delimiter)

Note: for a sentence like "Mrs. Havisham's class was impeccable (as far as one could see!) in all aspects." The tokenizer will correctly discern that the period at the end of Mrs. is not an EOS, however it will incorrectly mark the ! within the parentheses as an EOS and split "in all aspects." as a second sentence.

blocks|key|958891|text|使用正则表达式将文本分割成句子，在正则表达式中使用正则表达式，但在java中我不知道。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|958892|代码|958893|文本语句=Regex.Split(+string[]，@"(?<='""a-za-z.!\?)\s%2B(?=A-Z)")；|958894|90%25的作品|958895|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|L|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|M|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

use regular expression for split text into sentences,
in use Regex but in java i dont know.

code

string[] sentences = Regex.Split(text, @"(?&lt;=['""a-za-z][\)][\.\!\?])\s+(?=[A-Z])");

90% works

blocks|key|1726315|text|您可以使用document+preprocessor。真的很简单。只需输入一个文件名即可。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1726316|++++for+(List<HasWord>+sentence+:+new+DocumentPreprocessor(pathto/filename.txt))+{
+++++++++//sentence+is+a+list+of+words+in+a+sentence
++++}|code-block|syntax|javascript|1726317|entityMap|0|LINK|mutability|MUTABLE|url|http://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/process/DocumentPreprocessor.html^0|5|L|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

You can use the <a href="http://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/process/DocumentPreprocessor.html" rel="nofollow">document preprocessor</a>. It's really easy. Just feed it a filename.

<pre><code> for (List&lt;HasWord&gt; sentence : new DocumentPreprocessor(pathto/filename.txt)) {
 //sentence is a list of words in a sentence
 }
</code></pre>

blocks|key|4996430|text|对于公认的答案，有几个问题。首先，标记器将一些字符转换为两个字符的。其次，将标记化的文本与空格连接在一起并不会返回与之前相同的结果。因此，来自接受答案的示例文本以非平凡的方式转换输入文本。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4996431|但是，记号赋予器使用的CoreLabel类会跟踪它们映射到的源字符，因此，如果您有原始的字符串，那么重新构建正确的字符串是很容易的。|offset|length|style|CODE|4996432|下面的方法1显示了接受答案方法，方法2显示了我的方法，它克服了这些问题。|4996433|String+paragraph+=+"My+1st+sentence.+“Does+it+work+for+questions?”+My+third+sentence.";

List<String>+sentenceList;

/*+**+APPROACH+1+(BAD!)+**+*/
Reader+reader+=+new+StringReader(paragraph);
DocumentPreprocessor+dp+=+new+DocumentPreprocessor(reader);
sentenceList+=+new+ArrayList<String>();
for+(List<HasWord>+sentence+:+dp)+{
++++sentenceList.add(Sentence.listToString(sentence));
}
System.out.println(StringUtils.join(sentenceList,+"+_+"));

/*+**+APPROACH+2+**+*/
////+Tokenize
List<CoreLabel>+tokens+=+new+ArrayList<CoreLabel>();
PTBTokenizer<CoreLabel>+tokenizer+=+new+PTBTokenizer<CoreLabel>(new+StringReader(paragraph),+new+CoreLabelTokenFactory(),+"");
while+(tokenizer.hasNext())+{
++++tokens.add(tokenizer.next());
}
////+Split+sentences+from+tokens
List<List<CoreLabel>>+sentences+=+new+WordToSentenceProcessor<CoreLabel>().process(tokens);
////+Join+back+together
int+end;
int+start+=+0;
sentenceList+=+new+ArrayList<String>();
for+(List<CoreLabel>+sentence:+sentences)+{
++++end+=+sentence.get(sentence.size()-1).endPosition();
++++sentenceList.add(paragraph.substring(start,+end).trim());
++++start+=+end;
}
System.out.println(StringUtils.join(sentenceList,+"+_+"));|code-block|syntax|javascript|4996434|这将输出以下内容：|4996435|My+1st+sentence+.+_+``+Does+it+work+for+questions+?+''+_+My+third+sentence+.
My+1st+sentence.+_+“Does+it+work+for+questions?”+_+My+third+sentence.|4996436|entityMap^0|0|B|9|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|V|8|@$D|W|E|X|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|Y|8|@]|9|@]|A|$]]|$1|J|3|K|5|L|7|Z|8|@]|9|@]|A|$M|N]]|$1|O|3|P|5|6|7|10|8|@]|9|@]|A|$]]|$1|Q|3|R|5|L|7|11|8|@]|9|@]|A|$M|N]]|$1|S|3|-4|5|6|7|12|8|@]|9|@]|A|$]]]|T|$]]

There are a couple issues with the accepted answer. First, the tokenizer transforms some characters, such as the character “ into the two characters ``. Second, joining the tokenized text back together with whitespace does not return the same result as before. Therefore, the example text from the accepted answer transforms the input text in non-trivial ways.

However, the <code>CoreLabel</code> class that the tokenizer uses keeps track of the source characters they are mapped to, so it is trivial to rebuild the proper string, if you have the original.

Approach 1 below shows the accepted answers approach, Approach 2 shows my approach, which overcomes these issues.

<pre><code>String paragraph = "My 1st sentence. “Does it work for questions?” My third sentence.";

List&lt;String&gt; sentenceList;

/* ** APPROACH 1 (BAD!) ** */
Reader reader = new StringReader(paragraph);
DocumentPreprocessor dp = new DocumentPreprocessor(reader);
sentenceList = new ArrayList&lt;String&gt;();
for (List&lt;HasWord&gt; sentence : dp) {
 sentenceList.add(Sentence.listToString(sentence));
}
System.out.println(StringUtils.join(sentenceList, " _ "));

/* ** APPROACH 2 ** */
//// Tokenize
List&lt;CoreLabel&gt; tokens = new ArrayList&lt;CoreLabel&gt;();
PTBTokenizer&lt;CoreLabel&gt; tokenizer = new PTBTokenizer&lt;CoreLabel&gt;(new StringReader(paragraph), new CoreLabelTokenFactory(), "");
while (tokenizer.hasNext()) {
 tokens.add(tokenizer.next());
}
//// Split sentences from tokens
List&lt;List&lt;CoreLabel&gt;&gt; sentences = new WordToSentenceProcessor&lt;CoreLabel&gt;().process(tokens);
//// Join back together
int end;
int start = 0;
sentenceList = new ArrayList&lt;String&gt;();
for (List&lt;CoreLabel&gt; sentence: sentences) {
 end = sentence.get(sentence.size()-1).endPosition();
 sentenceList.add(paragraph.substring(start, end).trim());
 start = end;
}
System.out.println(StringUtils.join(sentenceList, " _ "));
</code></pre>

This outputs:

<pre><code>My 1st sentence . _ `` Does it work for questions ? '' _ My third sentence .
My 1st sentence. _ “Does it work for questions?” _ My third sentence.
</code></pre>

blocks|key|958970|text|public+class+k+{

public+static+void+main(String+a[]){

++++String+str+=+"This+program+splits+a+string+based+on+space";
++++String[]+words+=+str.split("+");
++++for(String+s:words){
++++++++System.out.println(s);
++++}
++++str+=+"This+++++program++splits+a+string+based+on+space";
++++words+=+str.split("\\s%2B");
}
}|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|958971|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>public class k {

public static void main(String a[]){

 String str = "This program splits a string based on space";
 String[] words = str.split(" ");
 for(String s:words){
 System.out.println(s);
 }
 str = "This program splits a string based on space";
 words = str.split("\\s+");
}
}
</code></pre>

blocks|key|958991|text|你可以很容易地使用Stanford+tagger来实现这一点。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|958992|String+text+=+new+String("Your+text....");++//Your+own+text.
List<List<HasWord>>+tokenizedSentences+=+MaxentTagger.tokenizeText(new+StringReader(text));

for(List<CoreLabel>+act+:+tokenizedSentences)+++++++//Travel+trough+sentences
{
++++System.out.println(edu.stanford.nlp.ling.Sentence.listToString(act));+//This+is+your+sentence
}|code-block|syntax|javascript|958993|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

You can pretty easy use Stanford tagger for this.

<pre><code>String text = new String("Your text...."); //Your own text.
List&lt;List&lt;HasWord&gt;&gt; tokenizedSentences = MaxentTagger.tokenizeText(new StringReader(text));

for(List&lt;CoreLabel&gt; act : tokenizedSentences) //Travel trough sentences
{
 System.out.println(edu.stanford.nlp.ling.Sentence.listToString(act)); //This is your sentence
}
</code></pre>

blocks|key|710753|text|@Kevin答案中的一个变体将解决这个问题，如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|710754|for(CoreMap+sentence:+sentences)+{
++++++String+sentenceText+=+sentence.get(TextAnnotation.class)
}|code-block|syntax|javascript|710755|这将为您提供句子信息，而不会影响其他注释器。|710756|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

A variation in the @Kevin answer which will solve the question is as follows:

<pre><code>for(CoreMap sentence: sentences) {
 String sentenceText = sentence.get(TextAnnotation.class)
}
</code></pre>

which gets you the sentence information without bothering with the other annotators.

blocks|key|1732031|text|使用由斯坦福大学CoreNLP版本3.6.0或3.7.0提供的Simple+API。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1732032|下面是一个使用3.6.0的示例。它的工作原理与3.7.0完全相同。|1732033|Java代码片段|1732034|import+java.util.List;

import+edu.stanford.nlp.simple.Document;
import+edu.stanford.nlp.simple.Sentence;
public+class+TestSplitSentences+{
++++public+static+void+main(String[]+args)+{
++++++++Document+doc+=+new+Document("The+text+paragraph.+Another+sentence.+Yet+another+sentence.");
++++++++List<Sentence>+sentences+=+doc.sentences();
++++++++sentences.stream().forEach(System.out::println);
++++}
}|code-block|syntax|javascript|1732035|收益率：|1732036|1732037|正文段落。|blockquote|1732038|1732039|另一句话。|1732040|又是一句话。|1732041|1732042|pom.xml|1732043|<?xml+version="1.0"+encoding="UTF-8"?>
<project+xmlns="http://maven.apache.org/POM/4.0.0"
+++++++++xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+++++++++xsi:schemaLocation="http://maven.apache.org/POM/4.0.0+http://maven.apache.org/xsd/maven-4.0.0.xsd">
++++<modelVersion>4.0.0</modelVersion>

++++<groupId>stanfordcorenlp</groupId>
++++<artifactId>stanfordcorenlp</artifactId>
++++<version>1.0-SNAPSHOT</version>

++++<properties>
++++++++<maven.compiler.source>1.8</maven.compiler.source>
++++++++<maven.compiler.target>1.8</maven.compiler.target>
++++</properties>

++++<dependencies>
++++++++
++++++++<dependency>
++++++++++++<groupId>edu.stanford.nlp</groupId>
++++++++++++<artifactId>stanford-corenlp</artifactId>
++++++++++++<version>3.6.0</version>
++++++++</dependency>
++++++++
++++++++<dependency>
++++++++++++<groupId>com.google.protobuf</groupId>
++++++++++++<artifactId>protobuf-java</artifactId>
++++++++++++<version>2.6.1</version>
++++++++</dependency>
++++</dependencies>
</project>|1732044|entityMap|0|LINK|mutability|MUTABLE|url|http://stanfordnlp.github.io/CoreNLP/#about^0|V|A|0|0|0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1A|8|@]|9|@$A|1B|B|1C|1|1D]]|C|$]]|$1|D|3|E|5|6|7|1E|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|1F|8|@]|9|@]|C|$]]|$1|H|3|I|5|J|7|1G|8|@]|9|@]|C|$K|L]]|$1|M|3|N|5|6|7|1H|8|@]|9|@]|C|$]]|$1|O|3|-4|5|6|7|1I|8|@]|9|@]|C|$]]|$1|P|3|Q|5|R|7|1J|8|@]|9|@]|C|$]]|$1|S|3|-4|5|6|7|1K|8|@]|9|@]|C|$]]|$1|T|3|U|5|6|7|1L|8|@]|9|@]|C|$]]|$1|V|3|W|5|6|7|1M|8|@]|9|@]|C|$]]|$1|X|3|-4|5|6|7|1N|8|@]|9|@]|C|$]]|$1|Y|3|Z|5|6|7|1O|8|@]|9|@]|C|$]]|$1|10|3|11|5|J|7|1P|8|@]|9|@]|C|$K|L]]|$1|12|3|-4|5|6|7|1Q|8|@]|9|@]|C|$]]]|13|$14|$5|15|16|17|C|$18|19]]]]

With the <a href="http://stanfordnlp.github.io/CoreNLP/#about" rel="nofollow noreferrer">Simple API</a> provided by Stanford CoreNLP version 3.6.0 or 3.7.0.
Here's an example with 3.6.0. It works exactly the same with 3.7.0.
Java Code Snippet
<pre><code>import java.util.List;

import edu.stanford.nlp.simple.Document;
import edu.stanford.nlp.simple.Sentence;
public class TestSplitSentences {
 public static void main(String[] args) {
 Document doc = new Document(&quot;The text paragraph. Another sentence. Yet another sentence.&quot;);
 List&lt;Sentence&gt; sentences = doc.sentences();
 sentences.stream().forEach(System.out::println);
 }
}
</code></pre>
Yields:
<blockquote>
The text paragraph.
Another sentence.
Yet another sentence.
</blockquote>
pom.xml
<pre><code>&lt;?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?&gt;
&lt;project xmlns=&quot;http://maven.apache.org/POM/4.0.0&quot;
 xmlns:xsi=&quot;http://www.w3.org/2001/XMLSchema-instance&quot;
 xsi:schemaLocation=&quot;http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd&quot;&gt;
 &lt;modelVersion&gt;4.0.0&lt;/modelVersion&gt;

 &lt;groupId&gt;stanfordcorenlp&lt;/groupId&gt;
 &lt;artifactId&gt;stanfordcorenlp&lt;/artifactId&gt;
 &lt;version&gt;1.0-SNAPSHOT&lt;/version&gt;

 &lt;properties&gt;
 &lt;maven.compiler.source&gt;1.8&lt;/maven.compiler.source&gt;
 &lt;maven.compiler.target&gt;1.8&lt;/maven.compiler.target&gt;
 &lt;/properties&gt;

 &lt;dependencies&gt;
 &lt;!-- https://mvnrepository.com/artifact/edu.stanford.nlp/stanford-corenlp --&gt;
 &lt;dependency&gt;
 &lt;groupId&gt;edu.stanford.nlp&lt;/groupId&gt;
 &lt;artifactId&gt;stanford-corenlp&lt;/artifactId&gt;
 &lt;version&gt;3.6.0&lt;/version&gt;
 &lt;/dependency&gt;
 &lt;!-- https://mvnrepository.com/artifact/com.google.protobuf/protobuf-java --&gt;
 &lt;dependency&gt;
 &lt;groupId&gt;com.google.protobuf&lt;/groupId&gt;
 &lt;artifactId&gt;protobuf-java&lt;/artifactId&gt;
 &lt;version&gt;2.6.1&lt;/version&gt;
 &lt;/dependency&gt;
 &lt;/dependencies&gt;
&lt;/project&gt;
</code></pre>

blocks|key|959106|text|另一个要素是如何设置句子分隔符？最常见的方式，也就是默认的方式，是依赖于陈述句子结束的常见标点符号。从收集的语料库中提取可能会遇到其他文档格式，其中一种是每行都是它自己的句子。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|959107|要像在接受的答案中那样设置DocumentPreprocessor的分隔符，您可以使用setSentenceDelimiter(String)。要使用@Kevin回答中建议的管道方法，可以使用ssplit属性。例如，要使用上一段中提出的行尾方案，可以将属性ssplit.eolonly设置为true|offset|length|style|CODE|959108|entityMap^0|0|17|S|3K|E|41|4|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@$D|L|E|M|F|G]|$D|N|E|O|F|G]|$D|P|E|Q|F|G]]|9|@]|A|$]]|$1|H|3|-4|5|6|7|R|8|@]|9|@]|A|$]]]|I|$]]

Another element, not addressed except in a few downvoted answers, is how to set the sentence delimiters? The most common way, the default, is to depend up the common punctuation marks which state the end of a sentence. There are other document formats that one might face from drawing upon gathered corpora, one of which being each line is it's own sentence.

To set your delimiters for the DocumentPreprocessor as in the accepted answers, you would use <code>setSentenceDelimiter(String)</code>. To use the pipeline approach suggested as in the answer by @Kevin, one would work with the ssplit properties. For example, to use the end of line scheme proposed in the previous paragraph, one would set the property <code>ssplit.eolonly</code> to <code>true</code>

blocks|key|1726416|text|在下面的代码中添加输入和输出文件的路径：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1726417|import+java.util.*;
import+edu.stanford.nlp.pipeline.*;
import+java.io.BufferedReader;
import+java.io.BufferedWriter;
import+java.io.FileReader;
import+java.io.FileWriter;
import+java.io.IOException;
import+java.io.PrintWriter;
public+class+NLPExample
{
++++public+static+void+main(String[]+args)+throws+IOException+
++++{
++++++++PrintWriter+out;
++++++++out+=+new+PrintWriter("C:\\Users\\ACER\\Downloads\\stanford-corenlp-full-+++++
++++++++2018-02-27\\output.txt");
++++++++Properties+props=new+Properties();
++++++++props.setProperty("annotators","tokenize,+ssplit,+pos,lemma");
++++++++StanfordCoreNLP+pipeline+=+new+StanfordCoreNLP(props);
++++++++Annotation+annotation;++
++++++++String+readString+=+null;
++++++++PrintWriter+pw+=+null;
++++++++BufferedReader+br+=+null;
++++++++br+=+new+BufferedReader+(new+
++++++++FileReader("C:\\Users\\ACER\\Downloads\\stanford-+
++++++++corenlp-full-2018-02-27\\input.txt"+)++)+;
++++++++pw+=+new+PrintWriter+(+new+BufferedWriter+(+new+FileWriter+(+
++++++++"C:\\Users\\ACER\\Downloads\\stanford-corenlp-full-2018-02-+++
++++++++27\\output.txt",false+
++++++++)))+;++++++
++++++++String+x+=+null;
++++++++while++((+readString+=+br.readLine+())++!=+null)
++++++++{
++++++++++++pw.println+(+readString+)+;+String+
++++++++++++xx=readString;x=xx;//System.out.println("OKKKKK");+
++++++++++++annotation+=+new+Annotation(x);
++++++++++++pipeline.annotate(annotation);++++//System.out.println("LamoohAKA");
++++++++++++pipeline.prettyPrint(annotation,+out);
++++++++}
++++++++br.close+(++)+;
++++++++pw.close+(++)+;
++++++++System.out.println("Done...");
++++}++++
}|code-block|syntax|javascript|1726418|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Add Path for input and output file in below code:-

<pre><code>import java.util.*;
import edu.stanford.nlp.pipeline.*;
import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.io.PrintWriter;
public class NLPExample
{
 public static void main(String[] args) throws IOException 
 {
 PrintWriter out;
 out = new PrintWriter("C:\\Users\\ACER\\Downloads\\stanford-corenlp-full- 
 2018-02-27\\output.txt");
 Properties props=new Properties();
 props.setProperty("annotators","tokenize, ssplit, pos,lemma");
 StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
 Annotation annotation; 
 String readString = null;
 PrintWriter pw = null;
 BufferedReader br = null;
 br = new BufferedReader (new 
 FileReader("C:\\Users\\ACER\\Downloads\\stanford- 
 corenlp-full-2018-02-27\\input.txt" ) ) ;
 pw = new PrintWriter ( new BufferedWriter ( new FileWriter ( 
 "C:\\Users\\ACER\\Downloads\\stanford-corenlp-full-2018-02- 
 27\\output.txt",false 
 ))) ; 
 String x = null;
 while (( readString = br.readLine ()) != null)
 {
 pw.println ( readString ) ; String 
 xx=readString;x=xx;//System.out.println("OKKKKK"); 
 annotation = new Annotation(x);
 pipeline.annotate(annotation); //System.out.println("LamoohAKA");
 pipeline.prettyPrint(annotation, out);
 }
 br.close ( ) ;
 pw.close ( ) ;
 System.out.println("Done...");
 } 
}
</code></pre>

How can I split a text or paragraph into sentences using <a href="http://nlp.stanford.edu/software/lex-parser.shtml" rel="noreferrer">Stanford parser</a>?

Is there any method that can extract sentences, such as <code>getSentencesFromString()</code> as it's provided for <a href="http://stanfordparser.rubyforge.org/" rel="noreferrer">Ruby</a>?

How can I split a text into sentences using the Stanford parser?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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如何使用将文本或段落拆分成句子有没有什么方法可以提取句子，比如为提供的getSentencesFromString()

问如何使用斯坦福解析器将文本拆分成句子？
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问如何使用斯坦福解析器将文本拆分成句子？EN