如何使用斯坦福分析器(Stanford parser)将一个文本或段落分割成句子?
是否有一种方法可以提取句子,例如像Ruby中提供的getSentencesFromString()
方法?
如何使用斯坦福分析器(Stanford parser)将一个文本或段落分割成句子?
是否有一种方法可以提取句子,例如像Ruby中提供的getSentencesFromString()
方法?
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);
}
我知道已经有一个被接受的答案了...但通常你只需要从一个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(在页面中间位置)
如果您只需要查找句子,可以从管道初始化中删除后续步骤,例如“解析”和“dcoref”,这将为您节省一些负载和处理时间。摇滚并且快速执行。
~K
接受的答案存在几个问题。首先,分词器会转换一些字符,例如字符“”被转换为两个字符``。其次,使用空格将标记化的文本拼接在一起并不能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微不足道的方式转换了输入文本。
然而,分词器使用的CoreLabel
类会跟踪它们映射到的源字符,如果您有原始字符,则重新构建正确的字符串很容易。
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,可以克服这些问题。
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, " _ "));
这将输出:
My 1st sentence . _ `` Does it work for questions ? '' _ My third sentence .
My 1st sentence. _ “Does it work for questions?” _ My third sentence.
Sentence.listToOriginalTextString
的方法,该方法接受您代码中的List<CoreLabel> sentence
变量。它还提到PTBT分词器需要使用"invertible=true"
选项运行。 - Sudhipublic 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: - 这句话的字符具有一定的魅力,通常可以在标点和散文中找到。这是第二个句子吗?确实是。
输出: 3个句子('?'被视为句子结束符)
注意:对于像“哈维舍夫人的班级在所有方面都无可挑剔(就我所知!)”这样的句子,分词器将正确地辨别出Mrs.结尾处的句号不是EOS,但是它会错误地将括号内的感叹号!视为EOS,并将“在所有方面。”分成第二个句子。
使用由Stanford CoreNLP版本3.6.0或3.7.0提供的简单API。
以下是3.6.0版本的示例。在3.7.0版本中完全相同。
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);
}
}
输出:
文本段落。
另外一个句子。
还有另一个句子。
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>
for (List<HasWord> sentence : new DocumentPreprocessor(pathto/filename.txt)) {
//sentence is a list of words in a sentence
}
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
}
for(CoreMap sentence: sentences) {
String sentenceText = sentence.get(TextAnnotation.class)
}
这将为您提供句子信息,而不会打扰其他注释器。
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...");
}
}
另一个元素,除了一些被踩的答案之外,没有得到解决,那就是如何设置句子分隔符?最常见的方式,也是默认方式,是依赖于标点符号来表示句子的结尾。还有其他文档格式,可能会从收集的语料库中面临,其中之一是每行都是一个句子。
要像接受的答案一样为DocumentPreprocessor设置分隔符,您可以使用setSentenceDelimiter(String)
。要使用@Kevin提出的管道方法,可以使用ssplit属性。例如,要使用上一段落提出的行末方案,可以将属性ssplit.eolonly
设置为true