TestStringSemanticDistance.java

package fr.inria.wimmics.prissma.test;

import java.util.List;

import edu.cmu.lti.jawjaw.pobj.POS;
import edu.cmu.lti.lexical_db.ILexicalDatabase;
import edu.cmu.lti.lexical_db.NictWordNet;
import edu.cmu.lti.lexical_db.data.Concept;
import edu.cmu.lti.ws4j.Relatedness;
import edu.cmu.lti.ws4j.RelatednessCalculator;
import edu.cmu.lti.ws4j.impl.HirstStOnge;
import edu.cmu.lti.ws4j.impl.JiangConrath;
import edu.cmu.lti.ws4j.impl.LeacockChodorow;
import edu.cmu.lti.ws4j.impl.Lesk;
import edu.cmu.lti.ws4j.impl.Lin;
import edu.cmu.lti.ws4j.impl.Path;
import edu.cmu.lti.ws4j.impl.Resnik;
import edu.cmu.lti.ws4j.impl.WuPalmer;
import edu.cmu.lti.ws4j.util.WS4JConfiguration;

public class TestStringSemanticDistance {

	private static ILexicalDatabase db = new NictWordNet();
    private static RelatednessCalculator lin = new Lin(db);
    private static RelatednessCalculator wup = new WuPalmer(db);
    private static RelatednessCalculator path = new Path(db);
    
    private static RelatednessCalculator lea = new LeacockChodorow(db);
    private static RelatednessCalculator hirst = new HirstStOnge(db);
    private static RelatednessCalculator lesk = new Lesk(db);
    private static RelatednessCalculator resnik = new Lesk(db);
    private static RelatednessCalculator jiang = new JiangConrath(db);
	 
	 
     private static RelatednessCalculator[] rcs = {
                     new HirstStOnge(db), new LeacockChodorow(db), new Lesk(db),  new WuPalmer(db), 
                     new Resnik(db), new JiangConrath(db), new Lin(db), new Path(db)
                     };
     
     private static void run( String word1, String word2 ) {
             WS4JConfiguration.getInstance().setMFS(true);
             for ( RelatednessCalculator rc : rcs ) {
                     double s = rc.calcRelatednessOfWords(word1, word2);
                     System.out.println( rc.getClass().getName()+"\t"+s );
             }
     }
     
     
     private static void runConcept(){
    	 ILexicalDatabase db = new NictWordNet();
    	 WS4JConfiguration.getInstance().setMFS(true);
    	 RelatednessCalculator rc = new WuPalmer(db);
    	 String word1 = "rock";
    	 String word2 = "jazz";
    	 List<POS[]> posPairs = rc.getPOSPairs();
    	 double maxScore = -1D;

    	 for(POS[] posPair: posPairs) {
    	     List<Concept> synsets1 = (List<Concept>)db.getAllConcepts(word1, posPair[0].toString());
    	     List<Concept> synsets2 = (List<Concept>)db.getAllConcepts(word2, posPair[1].toString());

    	     for(Concept synset1: synsets1) {
    	         for (Concept synset2: synsets2) {
    	             Relatedness relatedness = rc.calcRelatednessOfSynset(synset1, synset2);
    	             double score = relatedness.getScore();
    	             if (score > maxScore) { 
    	                 maxScore = score;
    	             }
    	         }
    	     }
    	 }

    	 if (maxScore == -1D) {
    	     maxScore = 0.0;
    	 }

    	 System.out.println("sim('" + word1 + "', '" + word2 + "') =  " + maxScore);
     }
     
     
     public static void main(String[] args) {
             long t0 = System.currentTimeMillis();
//             run( "add","add" );
             
//             String w1 = "add";
//             String w2 = "add";
//             System.out.println(lin.calcRelatednessOfWords(w1, w2));
//             System.out.println(wup.calcRelatednessOfWords(w1, w2));
//             System.out.println(path.calcRelatednessOfWords(w1, w2));
//             
//             System.out.println(lea.calcRelatednessOfWords(w1, w2));
//             System.out.println(hirst.calcRelatednessOfWords(w1, w2));
//             System.out.println(lesk.calcRelatednessOfWords(w1, w2));
//             System.out.println(resnik.calcRelatednessOfWords(w1, w2));
//             System.out.println(jiang.calcRelatednessOfWords(w1, w2));
             
                      
             
             runConcept();
             
             long t1 = System.currentTimeMillis();
             System.out.println( "Done in "+(t1-t0)+" msec." );
             
     }
     
     
     
     

}