java - 如何解决在 Web API 中获得的值与通过 ws4j 中的源获得的值之间的差异？

Question

我使用 ws4j 库开发了以下用于句子语义匹配的 API。但我没有得到语义相似性。输出作为图像附加，显示了冗余或 0 的值。是否有任何库错过了被调用？

package ws4jv01;

import edu.cmu.lti.lexical_db.ILexicalDatabase;
import edu.cmu.lti.lexical_db.NictWordNet;
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;

public class SentenceMatcherSimilarityMatrix
{
 private static ILexicalDatabase db = new NictWordNet();
 public double[][] getSimilarityMatrix( String[] words1, String[] words2, RelatednessCalculator rc )
{
    double[][] result = new double[words1.length][words2.length];
    for ( int i=0; i<words1.length; i++ ){
        for ( int j=0; j<words2.length; j++ ) {
            double score = rc.calcRelatednessOfWords(words1[i], words2[j]);
            result[i][j] = score;
          }
        }
    return result;
  }
  private void compute (String[] words1, String[] words2)
  {
    System.out.println("WuPalmer");
    RelatednessCalculator rc1 = new WuPalmer(db);
       {
        double[][] s1 = getSimilarityMatrix(words1, words2,rc1);
        for(int i=0; i<words1.length; i++){
            for(int j=0; j< words2.length; j++){
                System.out.print(s1[i][j] +"\t");
            } 
            System.out.println();
        }}
    System.out.println();
    System.out.println();

    System.out.println("Resnik");
    RelatednessCalculator rc2 = new Resnik(db);
    {
        double[][] s2 = getSimilarityMatrix(words1, words2,rc2);
        for(int i=0; i<words1.length; i++){
            for(int j=0; j< words2.length; j++){
                System.out.print(s2[i][j] +"\t");
            } 
            System.out.println();
        }}
    System.out.println();
    System.out.println();

    System.out.println("JiangConrath");
    RelatednessCalculator rc3 = new JiangConrath(db);
    {
        double[][] s2 = getSimilarityMatrix(words1, words2,rc3);
        for(int i=0; i<words1.length; i++){
            for(int j=0; j< words2.length; j++){
                System.out.print(s2[i][j] +"\t");
            } 
            System.out.println();
        }}
    System.out.println();
    System.out.println();

    System.out.println("Lin");
    RelatednessCalculator rc4 = new Lin(db);
    {
        double[][] s2 = getSimilarityMatrix(words1, words2,rc4);
        for(int i=0; i<words1.length; i++){
            for(int j=0; j< words2.length; j++){
                System.out.print(s2[i][j] +"\t");
            } 
            System.out.println();
        }}
    System.out.println();
    System.out.println();

    System.out.println("LeacockChodrow");
    RelatednessCalculator rc5 = new LeacockChodorow(db);
    {
        double[][] s2 = getSimilarityMatrix(words1, words2,rc5);
        for(int i=0; i<words1.length; i++){
            for(int j=0; j< words2.length; j++){
                System.out.print(s2[i][j] +"\t");
            } 
            System.out.println();
        }}
    System.out.println();
    System.out.println();

    System.out.println("Path");
    RelatednessCalculator rc6 = new Path(db);
    {
        double[][] s2 = getSimilarityMatrix(words1, words2,rc6);
        for(int i=0; i<words1.length; i++){
            for(int j=0; j< words2.length; j++){
                System.out.print(s2[i][j] +"\t");
            } 
            System.out.println();
        }}
    System.out.println();
    System.out.println();

    System.out.println("Lesk");
    RelatednessCalculator rc7 = new Lesk(db);
    {
        double[][] s2 = getSimilarityMatrix(words1, words2,rc7);
        for(int i=0; i<words1.length; i++){
            for(int j=0; j< words2.length; j++){
                System.out.print(s2[i][j] +"\t");
            } 
            System.out.println();
        }}
    System.out.println();
    System.out.println();

    System.out.println("HirstStOnge");
    RelatednessCalculator rc8 = new HirstStOnge(db);
    {
        double[][] s2 = getSimilarityMatrix(words1, words2,rc8);

        for(int i=0; i<words1.length; i++){
            for(int j=0; j< words2.length; j++){
                System.out.print(s2[i][j] +"\t");
            } 
            System.out.println();
           }}
          }

public static void main(String[] args) 
{
    String sent1 = "The boy is playing with a dog.";
    String sent2 = "The kid is playing with his pet.";

    String[] words1 = sent1.split(" ");
    String[] words2 = sent2.split(" ");
    SentenceMatcherSimilarityMatrix sm1 = new SentenceMatcherSimilarityMatrix(); 
    sm1.compute(words1, words2);
}
}

结果集

Answer 1

相似度矩阵渲染的小优化：

for (int i = 0; i <= words1.length; i++) {
    for (int j = 0; j <= words2.length; j++) {
        if(i==0 && j==0) {
            System.out.print(" " + "\t"); 
        } else if(i==0) {
            System.out.print(words2[j-1] + "\t");
        } else if(j==0) {
            System.out.print(words1[i-1] + "\t");
        } else {
            System.out.print(s1[i-1][j-1] + "\t");
        }
    }
    System.out.println();
}

这会将矩阵渲染为：