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stanford core nlp java输出

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我试图使用Stanford Corenlp工具包来注释文本(使用Netbeans而不是命令行),我尝试使用http://nlp.stanford.edu/software/corenlp.shtml#Usage上提供的代码(使用Stanford CoreNLP API) . 问题是:有谁能告诉我我怎么做获取文件中的输出,以便我可以进一步处理它?

我已经尝试将图形和句子打印到控制台,只是为了查看内容 . 这样可行 . 基本上我需要的是返回带注释的文档,这样我就可以从我的主类中调用它并输出一个文本文件(如果可能的话) . 我正在尝试查看stanford corenlp的API,但由于缺乏经验,我不知道返回此类信息的最佳方法是什么 .

这是代码:

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 = "the quick fox jumps over the lazy dog";

    // 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);       
      }

      // this is the parse tree of the current sentence
      Tree tree = sentence.get(TreeAnnotation.class);

      // this is the Stanford dependency graph of the current sentence
      SemanticGraph dependencies = sentence.get(CollapsedCCProcessedDependenciesAnnotation.class);
    }

    // This is the coreference link graph
    // Each chain stores a set of mentions that link to each other,
    // along with a method for getting the most representative mention
    // Both sentence and token offsets start at 1!
    Map<Integer, CorefChain> graph = 
      document.get(CorefChainAnnotation.class);

1 回答

  • 24

    一旦您拥有代码示例中显示的任何或所有自然语言分析,您需要做的就是以普通的Java方式将它们发送到文件,例如,使用FileWriter进行文本格式输出 . 具体来说,这是一个简单的完整示例,显示发送到文件的输出(如果您给它适当的命令行参数):

    import java.io.*;
    import java.util.*;
    
    import edu.stanford.nlp.io.*;
    import edu.stanford.nlp.ling.*;
    import edu.stanford.nlp.pipeline.*;
    import edu.stanford.nlp.trees.*;
    import edu.stanford.nlp.util.*;
    
    public class StanfordCoreNlpDemo {
    
      public static void main(String[] args) throws IOException {
        PrintWriter out;
        if (args.length > 1) {
          out = new PrintWriter(args[1]);
        } else {
          out = new PrintWriter(System.out);
        }
        PrintWriter xmlOut = null;
        if (args.length > 2) {
          xmlOut = new PrintWriter(args[2]);
        }
    
        StanfordCoreNLP pipeline = new StanfordCoreNLP();
        Annotation annotation;
        if (args.length > 0) {
          annotation = new Annotation(IOUtils.slurpFileNoExceptions(args[0]));
        } else {
          annotation = new Annotation("Kosgi Santosh sent an email to Stanford University. He didn't get a reply.");
        }
    
        pipeline.annotate(annotation);
        pipeline.prettyPrint(annotation, out);
        if (xmlOut != null) {
          pipeline.xmlPrint(annotation, xmlOut);
        }
        // An Annotation is a Map and you can get and use the various analyses individually.
        // For instance, this gets the parse tree of the first sentence in the text.
        List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
        if (sentences != null && sentences.size() > 0) {
          CoreMap sentence = sentences.get(0);
          Tree tree = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);
          out.println();
          out.println("The first sentence parsed is:");
          tree.pennPrint(out);
        }
      }
    
    }
    

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