基于WordNet的英文同义词、近义词相似度评估及代码实现

源码地址：https://github.com/XBWer/WordSimilarity

 

   1.确定要解决的问题及意义

在基于代码片段的分类过程中，由于程序员对数据变量名的选取可能具有一定的规范性，在某一特定业务处理逻辑代码中，可能多个变量名之间具有关联性或相似性（如“trade”（商品交易）类中，可能存在“business”，“transaction”，“deal”等同义词），在某些情况下，它们以不同的词语表达了相同的含义。因此，为了能够对代码片段做出更加科学的类别判断，更好地识别这些同义词，我们有必要寻找一种能够解决避免由于同义词的存在而导致误分类的方法。说白了，就是要去判断词语之间的相似度（即确定是否为近义词），并找出代码段中出现次数最多的一组语义。

 

2.要达到的效果

即在给定的代码段中，能够发现哪些词是属于同义词，并且能够实现分类。

Eg.public static void function(){

      String trade=”money”;

      Int deal=5;

      Long long business=0xfffffff;

      Boolen transaction=TRUE;

      ……

}

Output：同义词有：trade，deal，business，transaction

这段代码很可能与trade有关

 

3.初识WordNet

问题确定了之后，通过网上的搜索，发现了WordNet和word2vec这两个相关的词汇。（后知后觉，这本身就是一个找近义词的过程）

 

　　3.1 WordNet是什么

首先，来看WordNet。搜了一下相关介绍：

WordNet是一个由普林斯顿大学认识科学实验室在心理学教授乔治·A·米勒的指导下建立和维护的英语字典。开发工作从1985年开始，从此以后该项目接受了超过300万美元的资助（主要来源于对机器翻译有兴趣的政府机构）。

由于它包含了语义信息，所以有别于通常意义上的字典。WordNet根据词条的意义将它们分组，每一个具有相同意义的字条组称为一个synset（同义词集合）。WordNet为每一个synset提供了简短，概要的定义，并记录不同synset之间的语义关系。

WordNet的开发有两个目的：

它既是一个字典，又是一个辞典，它比单纯的辞典或词典都更加易于使用。

支持自动的文本分析以及人工智能应用。

WordNet内部结构

在WordNet中，名词，动词，形容词和副词各自被组织成一个同义词的网络，每个同义词集合都代表一个基本的语义概念，并且这些集合之间也由各种关系连接。（一个多义词将出现在它的每个意思的同义词集合中）。在WordNet的第一版中（标记为1.x），四种不同词性的网络之间并无连接。WordNet的名词网络是第一个发展起来的。

名词网络的主干是蕴涵关系的层次（上位／下位关系），它占据了关系中的将近80%。层次中的最顶层是11个抽象概念，称为基本类别始点（unique beginners），例如实体（entity，“有生命的或无生命的具体存在”），心理特征（psychological feature，“生命有机体的精神上的特征）。名词层次中最深的层次是16个节点。

                                                                                                                               （wikipedia）

         通俗地来说，WordNet是一个结构化很好的知识库，它不但包括一般的词典功能，另外还有词的分类信息。目前，基于WordNet的方法相对来说比较成熟，比如路径方法 (lch)、基于信息论方法(res)等。（详见参考文献）

    3.2 WordNet的安装与配置

有了WordNet ,也就等于是有了我们所要的单词库。所以，暂时先不考虑相似度的计算，把WordNet下载下来再说。

    参考http://hi.baidu.com/buptyoyo/item/f13dfe463c061e3afb896028。顺利地下载，安装以及跑demo。

    之后，一起来看一下WordNet的文件结构：

 

    bin目录下，有可执行文件WordNet 2.1.exe：

 

  

           可以看到，WordNet对所有的英文单词都进行的分类，并且形成了一棵语义树。在本例中，entity——>abstract entity——>abstraction——>attribute——>state——>feeling——> emotion——>love;

从叶子节点到根节点

     WordNet名次分类中的25个基本类：

 

dict目录里面存放的就是资源库了，可以看到，它以形容词，副词，名词，动词来分类：

 

doc为WordNet的用户手册文件文件夹

lib为WordNet软件使用Windows资源的函数库

src为源码文件夹

4.解决问题的大致思路

我们首先以 WordNet 的词汇语义分类作为基础，抽取出其中的同义词，然后采用基于向量空间的方法计算出相似度。工作流程如下：

 

5.基于WordNet的相似度计算

         以下摘自：《基于WordNet的英语词语相似度计算》

         5.1   特征提取

 

         5.2   意义相似度和词语相似度的计算

 

 

 

 6.实现效果

　　　

与“trade”的相似度比较：

分析：

 

先看第一组：trade vs trade

 

自己和自己当然是相似度100%

 

再看第二组：trade#n#5   vs deal#n#1

 

相似度竟然和第一组是一样的！根据结果，trade作为名词时，它的第5种含义和deal作为名词时的第1种含义是完全相似的。让我们去库里看个究竟：

　　trade#n#5：

deal#n#1：

再来看一组不是很好理解的：

trade#n#7   vs deal#n#2

 

    他们的相似度达到了0.14+，算是比较高的了，这是为什么呢？

 

　 trade#n#7：

sunshine#n#2:

相信聪明的你一定明白了为什么。

 

与“cat”的相似度比较：

  7.代码分析

工程结构图：

　　　　　　　　　　　　　　　　

  test.java

package JWordNetSim.test;

import java.io.FileInputStream;
import java.util.HashMap;
import java.util.Map;

import net.didion.jwnl.JWNL;
import net.didion.jwnl.data.IndexWord;
import net.didion.jwnl.data.POS;
import net.didion.jwnl.dictionary.Dictionary;
import shef.nlp.wordnet.similarity.SimilarityMeasure;

/**
 * A simple test of this WordNet similarity library.
 * @author Mark A. Greenwood
 */
public class Test
{
    public static void main(String[] args) throws Exception
    {    
        //在运行代码前，必须在本机上安装wordnet2.0，只能装2.0，装了2.1会出错
        JWNL.initialize(new FileInputStream("D:\JAVAProjectWorkSpace\jwnl\JWordNetSim\test\wordnet.xml"));
        
        //建议一个映射去配置相关参数
        Map<String,String> params = new HashMap<String,String>();
        
        //the simType parameter is the class name of the measure to use
        params.put("simType","shef.nlp.wordnet.similarity.JCn");
        
        //this param should be the URL to an infocontent file (if required
        //by the similarity measure being loaded)
        params.put("infocontent","file:D:\JAVAProjectWorkSpace\jwnl\JWordNetSim\test\ic-bnc-resnik-add1.dat");
        
        //this param should be the URL to a mapping file if the
        //user needs to make synset mappings
        params.put("mapping","file:D:\JAVAProjectWorkSpace\jwnl\JWordNetSim\test\domain_independent.txt");
        
        //create the similarity measure
        SimilarityMeasure sim = SimilarityMeasure.newInstance(params);
        
        //取词
//        Dictionary dict = Dictionary.getInstance();        
//        IndexWord word1 = dict.getIndexWord(POS.NOUN, "trade");            //这里把trade和dog完全定义为名词来进行处理
//        IndexWord word2 = dict.getIndexWord(POS.NOUN,"dog");                //
//        
//        //and get the similarity between the first senses of each word
//        System.out.println(word1.getLemma()+"#"+word1.getPOS().getKey()+"#1  " + word2.getLemma()+"#"+word2.getPOS().getKey()+"#1  " + sim.getSimilarity(word1.getSense(1), word2.getSense(1)));        
////        
//        //get similarity using the string methods (note this also makes use
//        //of the fake root node)
//        System.out.println(sim.getSimilarity("trade#n","deal#n"));
        
        //get a similarity that involves a mapping
        System.out.println(sim.getSimilarity("trade", "trade"));
        System.out.println(sim.getSimilarity("trade", "deal"));
        System.out.println(sim.getSimilarity("trade", "commerce"));
        System.out.println(sim.getSimilarity("trade", "transaction"));        
        System.out.println(sim.getSimilarity("trade", "finance"));
        System.out.println(sim.getSimilarity("trade", "financial"));
        System.out.println(sim.getSimilarity("trade", "business"));
        System.out.println(sim.getSimilarity("trade", "economy"));        
        System.out.println(sim.getSimilarity("trade", "school"));
        System.out.println(sim.getSimilarity("trade", "dog"));
        System.out.println(sim.getSimilarity("trade", "cat"));
        System.out.println(sim.getSimilarity("trade", "book"));
        System.out.println(sim.getSimilarity("trade", "sunshine"));
        System.out.println(sim.getSimilarity("trade", "smile"));
        System.out.println(sim.getSimilarity("trade", "nice"));
        System.out.println(sim.getSimilarity("trade", "hardly"));
        System.out.println(sim.getSimilarity("trade", "beautiful"));
    }
}

SimilarityMeasure.java

package shef.nlp.wordnet.similarity;

import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.net.URL;
import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.LinkedHashMap;
import java.util.Map;
import java.util.Set;

import net.didion.jwnl.JWNLException;
import net.didion.jwnl.data.IndexWord;
import net.didion.jwnl.data.POS;
import net.didion.jwnl.data.Synset;
import net.didion.jwnl.dictionary.Dictionary;

/**
 * An abstract notion of a similarity measure that all provided
 * implementations extend.
 * @author Mark A. Greenwood
 */
public abstract class SimilarityMeasure
{    
    /**
     * A mapping of terms to specific synsets. Usually used to map domain
     * terms to a restricted set of synsets but can also be used to map
     * named entity tags to appropriate synsets.
     */
    private Map<String,Set<Synset>> domainMappings = new HashMap<String,Set<Synset>>();
    
    /**
     * The maximum size the cache can grow to
     */
    private int cacheSize = 5000;
    
    /**
     * To speed up computation of the similarity between two synsets
     * we cache each similarity that is computed so we only have to
     * do each one once.
     */
    private Map<String,Double> cache = new LinkedHashMap<String,Double>(16,0.75f,true)
    {
        public boolean removeEldestEntry(Map.Entry<String,Double> eldest)
        {
            //if the size is less than zero then the user is asking us
            //not to limit the size of the cache so return false
            if (cacheSize < 0) return false;
            
            //if the cache has crown bigger than it's max size return true
            return size() > cacheSize;
        }
    }; 
    
    /**
     * Get a previously computed similarity between two synsets from the cache.
     * @param s1 the first synset between which we are looking for the similarity.
     * @param s2 the other synset between which we are looking for the similarity.
     * @return The similarity between the two sets or null
     *         if it is not in the cache.
     */
    protected final Double getFromCache(Synset s1, Synset s2)
    {
        return cache.get(s1.getKey()+"-"+s2.getKey());
    }
    
    /**
     * Add a computed similarity between two synsets to the cache so that
     * we don't have to compute it if it is needed in the future.
     * @param s1 one of the synsets between which we are storring a similarity.
     * @param s2 the other synset between which we are storring a similarity.
     * @param sim the similarity between the two supplied synsets.
     * @return the similarity score just added to the cache.
     */
    protected final double addToCache(Synset s1, Synset s2, double sim)
    {
        cache.put(s1.getKey()+"-"+s2.getKey(),sim);
        
        return sim;
    }
    
    /**
     * Configures the similarity measure using the supplied parameters.
     * @param params a set of key-value pairs that are used to configure
     *        the similarity measure. See concrete implementations for details
     *        of expected/possible parameters. 
     * @throws Exception if an error occurs while configuring the similarity measure.
     */
    protected abstract void config(Map<String,String> params) throws Exception;
    
    /**
     * Create a new instance of a similarity measure.
     * @param confURL the URL of a configuration file. Parameters are specified
     *        one per line as key:value pairs.
     * @return a new instance of a similairy measure as defined by the
     *         supplied configuration URL.
     * @throws Exception if an error occurs while creating the similarity measure.
     */
    public static SimilarityMeasure newInstance(URL confURL) throws Exception
    {
        //create map to hold the key-value pairs we are going to read from
        //the configuration file
        Map<String,String> params = new HashMap<String,String>();
        
        //create a reader for the config file
        BufferedReader in = null;
        
        try
        {
            //open the config file
            in = new BufferedReader(new InputStreamReader(confURL.openStream()));
                    
            String line = in.readLine();
            while (line != null)
            {
                line = line.trim();
                
                if (!line.equals(""))
                {
                    //if the line contains something then
                    
                    //split the data so we get the key and value
                    String[] data = line.split("\s*:\s*",2);
                    
                    if (data.length == 2)
                    {
                        //if the line is valid add the two parts to the map
                        params.put(data[0], data[1]);
                    }
                    else
                    {
                        //if the line isn't valid tell the user but continue on
                        //with the rest of the file
                        System.out.println("Config Line is Malformed: " + line);
                    }
                }
                
                //get the next line ready to process
                line = in.readLine();
            }
        }
        finally
        {
            //close the config file if it got opened
            if (in != null) in.close();
        }
        
        //create and return a new instance of the similarity measure specified
        //by the config file
        return newInstance(params);
    }
    
    /**
     * Creates a new instance of a similarity measure using the supplied parameters.
     * @param params a set of key-value pairs which define the similarity measure.
     * @return the newly created similarity measure.
     * @throws Exception if an error occurs  while creating the similarity measure.
     */
    public static SimilarityMeasure newInstance(Map<String,String> params) throws Exception
    {
        //get the class name of the implementation we need to load
        String name = params.remove("simType");
        
        //if the name hasn't been specified then throw an exception
        if (name == null) throw new Exception("Must specifiy the similarity measure to use");
        
        //Get hold of the class we need to load
        @SuppressWarnings("unchecked") Class<SimilarityMeasure> c = (Class<SimilarityMeasure>)Class.forName(name);
        
        //create a new instance of the similarity measure
        SimilarityMeasure sim = c.newInstance();
        
        //get the cache parameter from the config params
        String cSize = params.remove("cache");
        
        //if a cache size was specified then set it
        if (cSize != null) sim.cacheSize = Integer.parseInt(cSize);
        
        //get the url of the domain mapping file
        String mapURL = params.remove("mapping");
        
        if (mapURL != null)
        {
            //if a mapping file has been provided then 
                        
            //open a reader over the file
            BufferedReader in = new BufferedReader(new InputStreamReader((new URL(mapURL)).openStream()));
            
            //get the first line ready for processing
            String line = in.readLine();
            
            while (line != null)
            {
                if (!line.startsWith("#"))
                {
                    //if the line isn't a comment (i.e. it doesn't start with #) then...
                    
                    //split the line at the white space
                    String[] data = line.trim().split("\s+");
                    
                    //create a new set to hold the mapped synsets
                    Set<Synset> mappedTo = new HashSet<Synset>();
                    
                    for (int i = 1 ; i < data.length ; ++i)
                    {
                        //for each synset mapped to get the actual Synsets
                        //and store them in the set
                        mappedTo.addAll(sim.getSynsets(data[i]));
                    }
                    
                    //if we have found some actual synsets then
                    //store them in the domain mappings
                    if (mappedTo.size() > 0) sim.domainMappings.put(data[0], mappedTo);
                }
                
                //get the next line from the file
                line = in.readLine();
            }
            
            //we have finished with the mappings file so close it
            in.close();
        }        
        
        //make sure it is configured properly
        sim.config(params);
        
        //then return it
        return sim;
    }
    
    /**
     * This is the method responsible for computing the similarity between two
     * specific synsets. The method is implemented differently for each
     * similarity measure so see the subclasses for detailed information.
     * @param s1 one of the synsets between which we want to know the similarity.
     * @param s2 the other synset between which we want to know the similarity.
     * @return the similarity between the two synsets.
     * @throws JWNLException if an error occurs accessing WordNet.
     */
    public abstract double getSimilarity(Synset s1, Synset s2) throws JWNLException;
    
    /**
     * Get the similarity between two words. The words can be specified either
     * as just the word or in an encoded form including the POS tag and possibly
     * the sense number, i.e. cat#n#1 would specifiy the 1st sense of the noun cat.
     * @param w1 one of the words to compute similarity between.
     * @param w2 the other word to compute similarity between.
     * @return a SimilarityInfo instance detailing the similarity between the
     *         two words specified.
     * @throws JWNLException if an error occurs accessing WordNet.
     */
253     public final SimilarityInfo getSimilarity(String w1, String w2) throws JWNLException
    {
        //Get the (possibly) multiple synsets associated with each word
        Set<Synset> ss1 = getSynsets(w1);
        Set<Synset> ss2 = getSynsets(w2);
                
        //assume the words are not at all similar
        SimilarityInfo sim = null;
        
        for (Synset s1 : ss1)
263         {
264             for (Synset s2 : ss2)
265             {
266                 //for each pair of synsets get the similarity
267                 double score = getSimilarity(s1, s2);
268                                 
269                 if (sim == null || score > sim.getSimilarity())
270                 {
271                     //if the similarity is better than we have seen before
272                     //then create and store an info object describing the
273                     //similarity between the two synsets
274                     sim = new SimilarityInfo(w1, s1, w2, s2, score);
275                 }
276             }
277         }
        
        //return the maximum similarity we have found
        return sim;    
    }
    
    /**
     * Finds all the synsets associated with a specific word.
     * @param word the word we are interested. Note that this may be encoded
     *        to include information on POS tag and sense index.
     * @return a set of synsets that are associated with the supplied word
     * @throws JWNLException if an error occurs accessing WordNet
     */
    private final Set<Synset> getSynsets(String word) throws JWNLException
    {        
        //get a handle on the WordNet dictionary
        Dictionary dict = Dictionary.getInstance();
        
        //create an emptuy set to hold any synsets we find
        Set<Synset> synsets = new HashSet<Synset>();
        
        //split the word on the # characters so we can get at the
        //upto three componets that could be present: word, POS tag, sense index
        String[] data = word.split("#");
        
        //if the word is in the domainMappings then simply return the mappings
        if (domainMappings.containsKey(data[0])) return domainMappings.get(data[0]);
        
        if (data.length == 1)
        {
            //if there is just the word
                
            for (IndexWord iw : dict.lookupAllIndexWords(data[0]).getIndexWordArray())
            {
                //for each matching word in WordNet add all it's senses to
                //the set we are building up
                synsets.addAll(Arrays.asList(iw.getSenses()));
            }
            
            //we have finihsed so return the synsets we found
            return synsets;
        }
    
        //the calling method specified a POS tag as well so get that
        POS pos = POS.getPOSForKey(data[1]);
        
        //if the POS tag isn't valid throw an exception
        if (pos == null) throw new JWNLException("Invalid POS Tag: " + data[1]);
        
        //get the word with the specified POS tag from WordNet
        IndexWord iw = dict.getIndexWord(pos, data[0]);
        
        if (data.length > 2)
        {
            //if the calling method specified a sense index then
            //add just that sysnet to the set we are creating
            synsets.add(iw.getSense(Integer.parseInt(data[2])));
        }
        else
        {
            //no sense index was specified so add all the senses of
            //the word to the set we are creating
            synsets.addAll(Arrays.asList(iw.getSenses()));
        }
        
        //return the set of synsets we found for the specified word
        return synsets;
    }
}

  每个函数都有详细注解，大家应该都看的明白。

262~277的循环过程如下：

　　　　

  JCN.java

/************************************************************************
 *         Copyright (C) 2006-2007 The University of Sheffield          *
 *      Developed by Mark A. Greenwood <m.greenwood@dcs.shef.ac.uk>     *
 *                                                                      *
 * This program is free software; you can redistribute it and/or modify *
 * it under the terms of the GNU General Public License as published by *
 * the Free Software Foundation; either version 2 of the License, or    *
 * (at your option) any later version.                                  *
 *                                                                      *
 * This program is distributed in the hope that it will be useful,      *
 * but WITHOUT ANY WARRANTY; without even the implied warranty of       *
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        *
 * GNU General Public License for more details.                         *
 *                                                                      *
 * You should have received a copy of the GNU General Public License    *
 * along with this program; if not, write to the Free Software          *
 * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.            *
 ************************************************************************/

package shef.nlp.wordnet.similarity;

import net.didion.jwnl.JWNLException;
import net.didion.jwnl.data.Synset;

 25 /**
 26  * An implementation of the WordNet similarity measure developed by Jiang and
 27  * Conrath. For full details of the measure see:
 28  * <blockquote>Jiang J. and Conrath D. 1997. Semantic similarity based on corpus
 29  * statistics and lexical taxonomy. In Proceedings of International
 30  * Conference on Research in Computational Linguistics, Taiwan.</blockquote>
 31  * @author Mark A. Greenwood
 32  */
public class JCn extends ICMeasure
{
    /**
     * Instances of this similarity measure should be generated using the
     * factory methods of {@link SimilarityMeasure}.
     */
    protected JCn()
    {
        //A protected constructor to force the use of the newInstance method
    }
    
    @Override public double getSimilarity(Synset s1, Synset s2) throws JWNLException
    {
        //if the POS tags are not the same then return 0 as this measure
        //only works with 2 nouns or 2 verbs.
        if (!s1.getPOS().equals(s2.getPOS())) return 0;
        
        //see if the similarity is already cached and...
        Double cached = getFromCache(s1, s2);
        
        //if it is then simply return it
        if (cached != null) return cached.doubleValue();
        
        //Get the Information Content (IC) values for the two supplied synsets
        double ic1 = getIC(s1);
        double ic2 = getIC(s2);

        //if either IC value is zero then cache and return a sim of 0
        if (ic1 == 0 || ic2 == 0) return addToCache(s1,s2,0);
        
        //Get the Lowest Common Subsumer (LCS) of the two synsets
        Synset lcs = getLCSbyIC(s1,s2);
        
        //if there isn't an LCS then cache and return a sim of 0
        if (lcs == null) return addToCache(s1,s2,0);
        
        //get the IC valueof the LCS
        double icLCS = getIC(lcs);
        
        //compute the distance between the two synsets
        //NOTE: This is the original JCN measure
        double distance = ic1 + ic2 - (2 * icLCS);
        
        //assume the similarity between the synsets is 0
        double sim = 0;
        
        if (distance == 0)
        {
            //if the distance is 0 (i.e. ic1 + ic2 = 2 * icLCS) then...
            
            //get the root frequency for this POS tag
            double rootFreq = getFrequency(s1.getPOS());
            
            if (rootFreq > 0.01)
            {
                //if the root frequency has a value then use it to generate a
                //very large sim value
                sim = 1/-Math.log((rootFreq - 0.01) / rootFreq);
            }            
        }
        else
        {
            //this is the normal case so just convert the distance
            //to a similarity by taking the multiplicative inverse
            sim = 1/distance;
        }
        
        //cache and return the calculated similarity
        return addToCache(s1,s2,sim);
    }
}

LIN.java

package shef.nlp.wordnet.similarity;

import net.didion.jwnl.JWNLException;
import net.didion.jwnl.data.Synset;

/**
 * An implementation of the WordNet similarity measure developed by Lin. For
 * full details of the measure see:
 * <blockquote>Lin D. 1998. An information-theoretic definition of similarity. In
 * Proceedings of the 15th International Conference on Machine
 * Learning, Madison, WI.</blockquote>
 * @author Mark A. Greenwood
 */
public class Lin extends ICMeasure
{
    /**
     * Instances of this similarity measure should be generated using the
     * factory methods of {@link SimilarityMeasure}.
     */
    protected Lin()
    {
        //A protected constructor to force the use of the newInstance method
    }
    
    @Override public double getSimilarity(Synset s1, Synset s2) throws JWNLException
    {
        //if the POS tags are not the same then return 0 as this measure
        //only works with 2 nouns or 2 verbs.
        if (!s1.getPOS().equals(s2.getPOS())) return 0;
        
        //see if the similarity is already cached and...
        Double cached = getFromCache(s1, s2);
        
        //if it is then simply return it
        if (cached != null) return cached.doubleValue();
        
        //Get the Information Content (IC) values for the two supplied synsets
        double ic1 = getIC(s1);
        double ic2 = getIC(s2);
        
        //if either IC value is zero then cache and return a sim of 0
        if (ic1 == 0 || ic2 == 0) return addToCache(s1,s2,0);
        
        //Get the Lowest Common Subsumer (LCS) of the two synsets
        Synset lcs = getLCSbyIC(s1,s2);
        
        //if there isn't an LCS then cache and return a sim of 0
        if (lcs == null) return addToCache(s1,s2,0);
        
        //get the IC valueof the LCS
        double icLCS = getIC(lcs);
        
        //caluclaue the similarity score
        double sim = (2*icLCS)/(ic1+ic2);
        
        //cache and return the calculated similarity
        return addToCache(s1,s2,sim);
    }
}

参考文献：

《基于维基百科的语义相似度计算》盛志超，陶晓鹏(复旦大学计算机科学技术学院)；

《基于WordNet的英语词语相似度计算》颜伟，荀恩东（北京语言大学 语言信息处理研究所）

WordNet中的名词：http://ccl.pku.edu.cn/doubtfire/semantics/wordnet/c-wordnet/nouns-in-wordnet.htm

MIT的JWI（Java WordNet Interface）和JWNL（Java WordNet Library）比较  

http://jxr19830617.blog.163.com/blog/static/163573067201301985219857/

http://jxr19830617.blog.163.com/blog/static/1635730672013019105255295/