JWS同义词查询

本文主要是介绍JWS同义词查询，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

内容并不是原创，从网上找的一个现成的代码，但是我需要在这个代码上进行修改。所以先保存一份。

str1和str2中的词语会循环比较。

import edu.sussex.nlp.jws.JWS;
import edu.sussex.nlp.jws.Lin;public class TestExamples {private String str1;private String str2;private String dir = "C:/WordNet/";private JWS    ws = new JWS(dir, "2.1");public TestExamples(String str1,String str2){this.str1=str1;this.str2=str2;}public double getSimilarity(){String[] strs1 = splitString(str1);String[] strs2 = splitString(str2);double sum = 0.0;for(String s1 : strs1){for(String s2: strs2){double sc= maxScoreOfLin(s1,s2);sum+= sc;System.out.println("当前计算: "+s1+" VS "+s2+" 的相似度为:"+sc);}}double Similarity = sum /(strs1.length * strs2.length);sum=0;return Similarity;}private String[] splitString(String str){String[] ret = str.split(" ");return ret;}private double maxScoreOfLin(String str1,String str2){Lin lin = ws.getLin();double sc = lin.max(str1, str2, "n");if(sc==0){sc = lin.max(str1, str2, "v");}return sc;}public static void main(String args[]){String s1="was born in";String s2="birth place is";TestExamples sm= new TestExamples(s1, s2);System.out.println(sm.getSimilarity());}
}

结果如下：
Loading modules
set up:
... finding noun and verb <roots>
... calculating IC <roots> ...
... ICFinder
... DepthFinder
... PathFinder
... JiangAndConrath
... Lin
... Resnik
... Path
... WuAndPalmer
... Adapted Lesk : all relations
... Adapted Lesk (1)
... Adapted Lesk (2)
... HirstAndStOnge
... LeacockAndChodorow
... calculating depths of <roots> ...




Java WordNet::Similarity using WordNet 2.1 : loaded






当前计算: was VS birth 的相似度为:0.0
当前计算: was VS place 的相似度为:0.0
当前计算: was VS is 的相似度为:0.0
当前计算: born VS birth 的相似度为:0.0
当前计算: born VS place 的相似度为:0.0
当前计算: born VS is 的相似度为:0.0
当前计算: in VS birth 的相似度为:0.2853640399251109
当前计算: in VS place 的相似度为:0.3983913320550497
当前计算: in VS is 的相似度为:0.0
0.07597281910890673

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