测试站点：

先贴点代码，是stanfor-parser的demo：

import java.util.Collection;import java.util.List;import java.io.StringReader;import edu.stanford.nlp.process.TokenizerFactory;import edu.stanford.nlp.process.CoreLabelTokenFactory;import edu.stanford.nlp.process.DocumentPreprocessor;import edu.stanford.nlp.process.PTBTokenizer;import edu.stanford.nlp.ling.CoreLabel;import edu.stanford.nlp.ling.HasWord;import edu.stanford.nlp.ling.Sentence;import edu.stanford.nlp.trees.*;import edu.stanford.nlp.parser.lexparser.LexicalizedParser;class ParserDemo {  /**   * The main method demonstrates the easiest way to load a parser.   * Simply call loadModel and specify the path, which can either be a   * file or any resource in the classpath.  For example, this   * demonstrates loading from the models jar file, which you need to   * include in the classpath for ParserDemo to work.   */  public static void main(String[] args) {    LexicalizedParser lp = LexicalizedParser.loadModel("edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz");    if (args.length > 0) {      demoDP(lp, args[0]);    } else {      demoAPI(lp);    }  }  /**   * demoDP demonstrates turning a file into tokens and then parse   * trees.  Note that the trees are printed by calling pennPrint on   * the Tree object.  It is also possible to pass a PrintWriter to   * pennPrint if you want to capture the output.   */  public static void demoDP(LexicalizedParser lp, String filename) {    // This option shows loading and sentence-segmenting and tokenizing    // a file using DocumentPreprocessor.    TreebankLanguagePack tlp = new PennTreebankLanguagePack();    GrammaticalStructureFactory gsf = tlp.grammaticalStructureFactory();    // You could also create a tokenizer here (as below) and pass it    // to DocumentPreprocessor    for (List
     
       sentence : new DocumentPreprocessor(filename)) {      Tree parse = lp.apply(sentence);      parse.pennPrint();      System.out.println();      GrammaticalStructure gs = gsf.newGrammaticalStructure(parse);      Collection tdl = gs.typedDependenciesCCprocessed();      System.out.println(tdl);      System.out.println();    }  }  /**   * demoAPI demonstrates other ways of calling the parser with   * already tokenized text, or in some cases, raw text that needs to   * be tokenized as a single sentence.  Output is handled with a   * TreePrint object.  Note that the options used when creating the   * TreePrint can determine what results to print out.  Once again,   * one can capture the output by passing a PrintWriter to   * TreePrint.printTree.   */  public static void demoAPI(LexicalizedParser lp) {    // This option shows parsing a list of correctly tokenized words    String[] sent = { "This", "is", "an", "easy", "sentence", "." };    List
      
        rawWords = Sentence.toCoreLabelList(sent);    Tree parse = lp.apply(rawWords);    parse.pennPrint();    System.out.println();    // This option shows loading and using an explicit tokenizer    String sent2 = "This is another sentence.";    TokenizerFactory
       
         tokenizerFactory =      PTBTokenizer.factory(new CoreLabelTokenFactory(), "");    List
        
          rawWords2 =      tokenizerFactory.getTokenizer(new StringReader(sent2)).tokenize();    parse = lp.apply(rawWords2);    TreebankLanguagePack tlp = new PennTreebankLanguagePack();    GrammaticalStructureFactory gsf = tlp.grammaticalStructureFactory();    GrammaticalStructure gs = gsf.newGrammaticalStructure(parse);    List
         
           tdl = gs.typedDependenciesCCprocessed();    System.out.println(tdl);    System.out.println();    TreePrint tp = new TreePrint("penn,typedDependenciesCollapsed");    tp.printTree(parse);  }  private ParserDemo() {} // static methods only}
         
        
       
      
     

结果：

Your query猴子喜欢吃香蕉。Segmentation猴子喜欢吃香蕉。Tagging猴子/NR喜欢/VV吃/VV香蕉/NN。/PUParse(ROOT  (IP    (NP (NR 猴子))    (VP (VV 喜欢)      (IP        (VP (VV 吃)          (NP (NN 香蕉)))))    (PU 。)))Typed dependenciesnsubj(喜欢-2, 猴子-1)root(ROOT-0, 喜欢-2)ccomp(喜欢-2, 吃-3)dobj(吃-3, 香蕉-4)Typed dependencies, collapsednsubj(喜欢-2, 猴子-1)root(ROOT-0, 喜欢-2)ccomp(喜欢-2, 吃-3)dobj(吃-3, 香蕉-4)