Java 斯坦福解析器：如何打印解析树和通用依赖项？

我想从给定的文本行打印解析树和通用依赖项，如下面的演示所示

这是我的密码

public class ParseDoc {

    private final static String PCG_MODEL = "edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz";        

    private final TokenizerFactory<CoreLabel> tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "invertible=true");

    private static final LexicalizedParser parser = LexicalizedParser.loadModel(PCG_MODEL);

    public Tree parse(String str) {                
        List<CoreLabel> tokens = tokenize(str);
        Tree tree = parser.apply(tokens);
        return tree;
    }

    private List<CoreLabel> tokenize(String str) {
        Tokenizer<CoreLabel> tokenizer =
                tokenizerFactory.getTokenizer(
                        new StringReader(str));    
        return tokenizer.tokenize();
    }

    public static void main(String[] args) { 
        String str = "My dog also likes eating sausage.";
        // Parser parser = new Parser(); 
        Tree tree = parser.parse(str);  

        List<Tree> leaves = tree.getLeaves();
        // Print words and Pos Tags
        for (Tree leaf : leaves) { 
            Tree parent = leaf.parent(tree);
            System.out.print(leaf.label().value() + "-" + parent.label().value() + " ");
        }
        System.out.println(); 
    }
}

这对我没有任何用处

我要打印树：

(ROOT
  (S
    (NP (PRP$ My) (NN dog))
    (ADVP (RB also))
    (VP (VBZ likes)
      (S
        (VP (VBG eating)
          (NP (NN sausage)))))
    (. .)))

以及普遍依赖性：

nmod:poss(dog-2, My-1)
nsubj(likes-4, dog-2)
advmod(likes-4, also-3)
root(ROOT-0, likes-4)
xcomp(likes-4, eating-5)
dobj(eating-5, sausage-6)

---

如何实现这一点？

以下是一些示例代码：

package edu.stanford.nlp.examples;

import edu.stanford.nlp.pipeline.*;
import edu.stanford.nlp.ling.*;
import edu.stanford.nlp.trees.*;
import edu.stanford.nlp.semgraph.*;
import edu.stanford.nlp.util.*;

import java.util.*;

public class PrintParse {

  public static void main(String[] args) {
    Annotation document =
        new Annotation("My dog also likes eating sausage.");
    Properties props = new Properties();
    props.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,parse");
    StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
    pipeline.annotate(document);
    for (CoreMap sentence : document.get(CoreAnnotations.SentencesAnnotation.class)) {
      Tree constituencyParse = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);
      System.out.println(constituencyParse);
      SemanticGraph dependencyParse =
          sentence.get(SemanticGraphCoreAnnotations.BasicDependenciesAnnotation.class);
      System.out.println(dependencyParse.toList());
    }
  }

}

package edu.stanford.nlp.examples;

import edu.stanford.nlp.pipeline.*;
import edu.stanford.nlp.ling.*;
import edu.stanford.nlp.trees.*;
import edu.stanford.nlp.semgraph.*;
import edu.stanford.nlp.util.*;

import java.util.*;

public class PrintParse {

  public static void main(String[] args) {
    Annotation document =
        new Annotation("My dog also likes eating sausage.");
    Properties props = new Properties();
    props.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,parse");
    StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
    pipeline.annotate(document);
    for (CoreMap sentence : document.get(CoreAnnotations.SentencesAnnotation.class)) {
      Tree constituencyParse = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);
      System.out.println(constituencyParse);
      SemanticGraph dependencyParse =
          sentence.get(SemanticGraphCoreAnnotations.BasicDependenciesAnnotation.class);
      System.out.println(dependencyParse.toList());
    }
  }

}