如何使用斯坦福解析器将文本拆分为句子？

Question

如何使用Stanford parser 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getSentencesFromString()，因为它是为Ruby提供的？

Answer 1

您可以检查 DocumentPreprocessor 类。下面是一个简短的sn-p。我认为可能还有其他方法可以做你想做的事。

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);
}

Answer 2

我知道已经有一个公认的答案...但通常你只需从带注释的文档中获取 SentenceAnnotations。

// 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);       
  }

}

来源 - http://nlp.stanford.edu/software/corenlp.shtml（中途）

如果您只是在寻找句子，您可以从管道初始化中删除后面的步骤，如“parse”和“dcoref”，这将为您节省一些加载和处理时间。摇滚乐。 ~K

Answer 3

接受的答案有几个问题。首先，分词器将一些字符，例如字符“转换为两个字符”。其次，将标记化的文本与空格重新连接在一起不会返回与以前相同的结果。因此，接受答案中的示例文本以非平凡的方式转换输入文本。

但是，标记器使用的 CoreLabel 类会跟踪它们映射到的源字符，因此如果您有原始字符串，重建正确的字符串是微不足道的。

下面的方法 1 显示了接受的答案方法，方法 2 显示了我的方法，它克服了这些问题。

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, " _ "));

这个输出：

My 1st sentence . _ `` Does it work for questions ? '' _ My third sentence .
My 1st sentence. _ “Does it work for questions?” _ My third sentence.

Answer 4

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：

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));
    }
}

输入： - 这句话的人物有一定的魅力，在标点符号和散文中很常见。这是第二句？确实如此。

输出： 3 个句子（“？”被视为句尾分隔符）

注意：对于像“Havisham 夫人的课程在各个方面都无可挑剔（就人们所见！）”这样的句子。分词器会正确识别出 Mrs. 结尾的句号不是 EOS，但是它会错误地标记 !在括号内作为 EOS 并拆分“在所有方面”。作为第二句话。

Answer 5

使用 Stanford CoreNLP 3.6.0 或 3.7.0 版本提供的 Simple API。

以下是 3.6.0 的示例。它与 3.7.0 完全相同。

Java 代码片段

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);
    }
}

产量：

文本段落。

另一个句子。

还有一句话。

pom.xml

<?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>
        <!-- https://mvnrepository.com/artifact/edu.stanford.nlp/stanford-corenlp -->
        <dependency>
            <groupId>edu.stanford.nlp</groupId>
            <artifactId>stanford-corenlp</artifactId>
            <version>3.6.0</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/com.google.protobuf/protobuf-java -->
        <dependency>
            <groupId>com.google.protobuf</groupId>
            <artifactId>protobuf-java</artifactId>
            <version>2.6.1</version>
        </dependency>
    </dependencies>
</project>

Answer 6

您可以使用document preprocessor。这真的很容易。只需给它一个文件名。

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

Answer 7

你可以很容易地使用斯坦福标记器。

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
}

Answer 8

@Kevin 答案的一个变体将解决这个问题，如下所示：

for(CoreMap sentence: sentences) {
      String sentenceText = sentence.get(TextAnnotation.class)
}

它可以在不打扰其他注释器的情况下为您获取句子信息。

Answer 9

除了一些被否决的答案外，另一个没有解决的元素是如何设置句子分隔符？默认情况下，最常见的方法是依赖于表示句子结尾的常见标点符号。从收集的语料库中提取可能会面临其他文档格式，其中一种是每一行都是它自己的句子。

要按照接受的答案设置 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

Answer 10

在下面的代码中添加输入和输出文件的路径：-

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...");
    }    
}

Answer 11

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+");
}
}

Answer 12

使用正则表达式将文本拆分成句子， 在使用正则表达式，但在 java 中我不知道。

代码

string[] 句子 = Regex.Split(text, @"(?

90% 有效