一.问题提出:像这样的"[TVB2008][溏心风暴之家好月圆][国语][DVD-RMVB][第01集]|BT285.cn|BT下载|BT电影|BT软件"的标题,我们怎样提取出关键字"家好月圆",当然是在电影或是电视剧的范围内.
        二.不用词典,你用什么方式分词.

解决方法:
      1.针对此网站大部分都是BT下载,一般都是电影,电视剧,动漫.先对整个网站的标题进行Lucene索引.
      2.去掉一些常用符号如:数字,字母,标志符.如"[TVB2008][溏心风暴之家好月圆][国语][DVD-RMVB][第01集]|BT285.cn|BT下载|BT电影|BT软件" 变成"溏心风暴之家好月圆 国语 第 集"
      3.采用二元分法.如"溏心风暴之家好月圆 国语 第 集" 分拆成"溏心 心风 风暴 暴之 之家 家好 好月 圆 国语 第 集"
      4.采用统计方法,对一些高频词去掉,如"国语 第 集" 因为"国语" 出现次数3000次(注:数据库的数据有25万条),"第"与"集"也如此.
      5.对"溏心 心风 风暴 暴之 之家 家好 好月 圆 国语 第 集"进行分析并统计次数.
      6.按最长连接词拼凑.按一定的比例组合划分.
      7.得出"家好月圆"为其关键字

 上代码:

package com.wwy.lucene;

import java.net.URLEncoder;
import java.util.ArrayList;
import java.util.List;

import org.apache.log4j.Logger;
import org.apache.lucene.analysis.standard.StandardAnalyzer;
import org.apache.lucene.document.Document;
import org.apache.lucene.queryParser.QueryParser;
import org.apache.lucene.search.Hits;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.Searcher;
import org.springframework.context.support.ClassPathXmlApplicationContext;

import com.funinhand.orm.database.base.Page;
import com.funinhand.shld.dao.Bt285DAO;
import com.funinhand.shld.dao.WNewsDAO;
import com.funinhand.shld.pojo.Bt285;
import com.funinhand.shld.pojo.WNews;
import com.wwy.thread.Configure;

/** *//**
 * 本代码在下面的网站中测试过并取的60%左右的有效数.
 * 
 * http://www.bt285.cn BT下载 
 * http://www.yaonba.com.cn NBA中文网
 * http://www.5a520.cn 小说520网 
 * http://www.vagaaga.cn vagaaga
 *author by wangdei
 *2008-6-12 上午11:16:41
 *
 */

public class KeyWordLucene {
    
        private static Logger logger = Logger.getLogger(KeyWordLucene.class);
        
        public static String[] StopStrs = {"BT285","BT软件","BT电影","BT下载"};
        
        
        public static String path = null ;
        
        private List<DataModel> sentenceList = new ArrayList<DataModel>();
        
        public KeyWordLucene(){
            logger.debug("KeyWordLucene init");
        }
        public int queryMatchSize(String queryStr,String lucennePath){        
            Searcher searcher = null;
            try {
                searcher = new IndexSearcher(lucennePath);// "index"指定索引文件位置
                Document doc = new Document();
                StandardAnalyzer analyzer = new StandardAnalyzer(StopStrs);
                // 一段简单的检索代码
                QueryParser queryParser = new QueryParser("title", analyzer);
                Query query = queryParser.parse(queryStr);
                // 检索    
                Hits hits = searcher.search(query);
                int size = hits.length();
                searcher.close();
                return size;
            } catch (Exception e) {
                return 0;
            }finally{
                 if(searcher!=null){   
                     try {   
                         searcher.close();   
                     }   
                     catch (Exception e) {}   
                     searcher = null;   
                   }   

            }
            
        }
        
        
        public boolean stopWord(String word){
            char charAsi = word.toCharArray()[0];
            int intAsi = (int)charAsi;
            if(intAsi<127)
                return true;
            /**//*
             * 65293 -,12289 、,9670 ◆,9733 ★,12300 「,8545 Ⅱ,65281 ！,65288 （ ,65306 ：,
             */
            int[] asiExpert = {65293,12289,9670,9733,12300,8545,65281,65288,65306};
            for(int i=0;i<asiExpert.length;i++){
                if(asiExpert[i] == intAsi)
                    return true;
            }
            String[] str = {"【","】","[","]","."," ","/","\\","》","《","?","的"};
            for(int i=0;i<str.length;i++){
                if(word.equals(str[i]))
                    return true;
            }
            return false;
        }
        
        /** *//**
         * 返回整个句子的集合
         *  cat news Id=211562,【There.Will.Be.Blood.血色黑金.2007.Blu-ray.a1080.x264.DD51 D9】
             血色=165,j=21,time=23
             色黑=19,j=22,time=23
             黑金=86,j=23,time=23
         * @param news
         * @return
         */
        private void charMatch(WNews news){
            sentenceList.clear();
            String title = news.getTitle();
            int matchSize = 0;
            int length = title.length();
            for(int j=0;j<length-2;j++){
                if(stopWord(title.substring(j,j+1)) || stopWord(title.substring(j+1,j+2)))
                        continue;
                DataModel dataModel = new DataModel();
                long begin = System.currentTimeMillis();
                matchSize =queryMatchSize(title.substring(j,j+2), path);
                long needsTime = (System.currentTimeMillis()-begin);
                dataModel.setName(title.substring(j,j+2));
                dataModel.setSize(matchSize);
                dataModel.setLocation(j);
                dataModel.setNeedTime(needsTime);
                //logger.debug(dataModel.toString());
                sentenceList.add(dataModel);
            }
        }
        
        /** *//**
         * 提取字符串的.
         * [古天乐刘若英最新钜献][我们这一家][粤语中字][那些发垃圾信息的不是我]
         * [天乐=122,j=2,time=16,
         *  刘若=84,j=4,time=16, 
         *  若英=84,j=5,time=0,
         *  最新=10298,j=7,time=31, 
         * 钜献=11,j=9,time=16, 
         * 我们=441,j=13,time=15, 
         * 一家=518,j=16,time=16, 
         * 语中=31128,j=21,time=63,
         * 中字=36065,j=22,time=78,
         * 那些=17,j=26,time=16, 
         * 垃圾=24,j=29,time=94,
         * 信息=12,j=31,time=0, 
         * 不是=167,j=34,time=15]
         */
        private void pickChar(){
            List<DataModel> tempList = new ArrayList<DataModel>();
            int size = sentenceList.size();
            //取二元比较字符串
            int i = 0;
            while(i<size){                                    
                DataModel dataModel1 = sentenceList.get(i);
                if(i == size-1){
                    tempList.add(dataModel1);
                    sentenceList.clear();
                    setSentenceList(tempList);
                    return;
                }
                DataModel dataModel2 = sentenceList.get(i+1);
                int location1 = dataModel1.getLocation();
                int location2 = dataModel2.getLocation();
                //如果是相邻的
                if(location1 +1  == location2){
                    if(dataModel1.getSize()>=dataModel2.getSize())
                        tempList.add(dataModel1);
                    else
                        tempList.add(dataModel2);
                    i+=2;
                    if(i == size){
                        //加最后一个字
                        sentenceList.clear();
                        setSentenceList(tempList);
                        return;
                    }
                }else{
                    tempList.add(dataModel1);
                    //tempList.add(dataModel2);
                    i++;
                }
            }
            return;
        }
        
        /** *//**
         * 去掉出现机率大的字符:如美国,大片,影视,==
         *
         */
        private void delRateBigChar(){
            List<DataModel> tempList = new ArrayList<DataModel>();
            int size = sentenceList.size();
            int i = 0;
            while(i<size){                                    
                DataModel dataModel = sentenceList.get(i);
                if(dataModel.getSize()>1000){
                    i++;
                    continue;
                }
                tempList.add(dataModel);
                i++;
            }
            sentenceList.clear();
            setSentenceList(tempList);
        }
        
        /** *//**
         * 组合字符
         *
         */
        private String compisteChar(){
            List<DataModel> tempList = new ArrayList<DataModel>();
            int size = sentenceList.size();
            int i = 0;        
            while(i<size){
                int j = i;
                DataModel dataModel = new DataModel("",0,0);
                StringBuffer sb = new StringBuffer(); 
                boolean isFetch = false;
                while(j<size){                
                    DataModel dataModelJ1 = sentenceList.get(j);
                    int location1 = dataModelJ1.getLocation();            
                    if(j == size-1){
                        dataModel.setName(dataModel.getName() + dataModelJ1.getName());
                        dataModel.setLocation(dataModel.getLocation() + dataModelJ1.getLocation());
                        dataModel.setAfterLocation(dataModelJ1.getAfterLocation());
                        tempList.add(dataModel);
                        break;
                    }
                    DataModel dataModelJ2 = sentenceList.get(j+1);
                    int location2 = dataModelJ2.getLocation();
                    if(dataModel.getAfterLocation() +2 <location1 && isFetch){
                        tempList.add(dataModel);
                        break;
                    }                        
                    if(location1 +1==location2){
                        isFetch = true;
                        sb.append(dataModelJ1.getName() +dataModelJ2.getName().substring(1));
                        dataModel.setName(dataModel.getName() + sb.toString());
                        dataModel.setAfterLocation(dataModelJ2.getAfterLocation());
                        dataModel.setLocation(dataModel.getLocation() + dataModelJ1.getLocation()+ dataModelJ2.getLocation());
                    }
                    else if(location1 +2==location2){
                        isFetch = true;
                        sb.append(dataModelJ1.getName() +dataModelJ2.getName());
                        dataModel.setName(dataModel.getName() + sb.toString());
                        dataModel.setAfterLocation(dataModelJ2.getAfterLocation());
                        dataModel.setLocation(dataModel.getLocation() + dataModelJ2.getLocation());
                    }
                    else{
                        dataModel.setName(dataModel.getName() + sb.toString());
                        dataModel.setAfterLocation(dataModelJ2.getAfterLocation());
                        dataModel.setLocation(dataModel.getLocation() + dataModelJ1.getLocation()+ dataModelJ2.getLocation());
                        tempList.add(dataModel);
                        break;
                    }
                    j+=2;
                    if(j == size){
                        tempList.add(dataModel);
                        break;
                    }
                }
                i++;
            }
            //取最长的字符串
            int tempSize = tempList.size();
            //如果字符串都超过给定的最大值时,或是其他情况
            if(tempSize == 0)
                return null;
            DataModel dataModelInit = tempList.get(0);        
            for(int k=1;k<tempSize;k++){
                String nameInit = dataModelInit.getName();
                DataModel dataModel = tempList.get(k);
                String name = dataModel.getName();
                if(name.length()>nameInit.length())
                    dataModelInit.setName(name);
            }
            logger.info("getName()=" +dataModelInit.getName());
            return dataModelInit.getName();
        }
        public String seqAction(WNews news){
            if(logger.isDebugEnabled())
                logger.debug("KeyWordLucene seqAction is begin");
            charMatch(news);
            pickChar();
            delRateBigChar();
            return compisteChar();
        }
        public static void main(String[] args) throws Exception {
            System.out.println("server begin!");
            Configure.propertiesConfigure();
             path = Configure.getCreateBtLucenePath();
            ClassPathXmlApplicationContext appContext = new ClassPathXmlApplicationContext(
            "./mysqlContext.xml");
            WNewsDAO newsDAO = (WNewsDAO) appContext.getBean("wNewDaoProxy");
            Bt285DAO bt285DAO = (Bt285DAO)appContext.getBean("bt285DAO");
            KeyWordLucene action = new KeyWordLucene();
            for(int i=1;i<3;i++){
                try {
                    Thread.sleep(1000L);
                } catch (InterruptedException e) {
                    // TODO Auto-generated catch block
                    e.printStackTrace();
                }
                Page page = new Page();
                logger.info("i=" + i);
                page.setPageIndex(i);
                page.setPageSize(1000);
                List<WNews> list = bt285DAO.findPageByQuery(
                        "select t from wnews t ",null, page);
                for(WNews news:list){
                    //if(news.getId() != 212142)
                        //continue;
                    logger.debug("cat news Id=" + news.getId() +"," + news.getTitle());
                    action.seqAction(news);
                }
            }
            
            //action.batchCreate();        
            
            System.out.println(URLEncoder.encode("天兆","UTF-8"));//%E5%A4%A9%E5%85%86
            System.out.println("server finish!");
        }


        public List<DataModel> getSentenceList() {
            return sentenceList;
        }


        public void setSentenceList(List<DataModel> sentenceList) {
            this.sentenceList = sentenceList;
        }
}

测试用例:
日本H动漫下载)鬼作第五夜-杉本翔子 
[TVB2008][当狗爱上猫][国语][DVD-RMVB][04-05集]
夜来香社区官方网站
结论:60%有效.

不足之处:
 1.没有采用词典,而用二元分,速度慢,词有较大的二意性.
 2.有些词用统计方法不准确.
 3.干扰系数较大.

 续,准备采用词典