Lucene常用的Analyzer功能概述以及自定義Analyzer

CevenCheng 2012-05-12

展開全文

WhitespaceAnalyzer：僅僅是去除空格，對字符沒有l(wèi)owcase化,不支持中文，會保留原文中的破折號，以空格為邊界，將空格間的內容切分為最小的語匯單元。

SimpleAnalyzer：功能強于WhitespaceAnalyzer，將所有的字符lowcase化，不支持中文，保留停用詞，并以非字母字符作為單個語匯單元的邊界。

StopAnalyzer：StopAnalyzer的功能超越了SimpleAnalyzer，在SimpleAnalyzer的基礎上增加了去除StopWords的功能,不支持中文

StandardAnalyzer：英文的處理能力同于StopAnalyzer，保留XY&Z形式的單詞，且會把email地址保留下來。支持中文采用的方法為單字切分。

以上四個Analyzer可以用下例來說明：

輸入字符串：XY&Z mail is - xyz@sohu.com
=====Whitespace analyzer====
分析方法：空格分割
XY&Z

mail
is
-
xyz@sohu.com
=====Simple analyzer====
分析方法：空格及各種符號分割
xy
z
mail
is
xyz
sohu
com
=====stop analyzer====
分析方法：空格及各種符號分割,去掉停止詞，停止詞包括 is,are,in,on,the等無實際意義
的詞
xy
z
mail
xyz
sohu
com
=====standard analyzer====
分析方法：混合分割,包括了去掉停止詞，支持漢語
xy&z
mail
xyz@sohu.com

ChineseAnalyzer：來自于Lucene的sand box.性能類似于StandardAnalyzer，缺點是不支持中英文混和分詞。

CJKAnalyzer：chedong寫的CJKAnalyzer的功能在英文處理上的功能和StandardAnalyzer相同，但是在漢語的分詞上，不能過濾掉標點符號，即使用二元切分。

TjuChineseAnalyzer：自定義的，功能最為強大。TjuChineseAnlyzer的功能相當強大,在中文分詞方面由于其調用的為ICTCLAS的java接口.所以其在中文方面性能上同與ICTCLAS.其在英文分詞上采用了Lucene的StopAnalyzer,可以去除 stopWords,而且可以不區(qū)分大小寫，過濾掉各類標點符號。

各個Analyzer的功能已經(jīng)比較介紹完畢了,現(xiàn)在咱們應該學寫Analyzer,如何diy自己的analyzer呢??

如何DIY一個Analyzer

咱們寫一個Analyzer,要求有一下功能
(1)    可以處理中文和英文,對于中文實現(xiàn)的是單字切分,對于英文實現(xiàn)的是以空格切分.
(2)    對于英文部分要進行小寫化.
(3)    具有過濾功能,可以人工設定StopWords列表.如果不是人工設定,系統(tǒng)會給出默認的StopWords列表.
(4)    使用P-stemming算法對于英文部分進行詞綴處理.

代碼如下:

public final class DiyAnalyzer
    extends Analyzer
{
private Set stopWords;
public static final String[] CHINESE_ENGLISH_STOP_WORDS =
      {
      "a", "an", "and", "are", "as", "at", "be", "but", "by",
      "for", "if", "in", "into", "is", "it",
      "no", "not", "of", "on", "or", "s", "such",
      "t", "that", "the", "their", "then", "there", "these",
      "they", "this", "to", "was", "will", "with",
      "我", "我們"
};

public DiyAnalyzer()
{
this.stopWords=StopFilter.makeStopSet(CHINESE_ENGLISH_STOP_WORDS);
}

public DiyAnalyzer(String[] stopWordList)
{
this.stopWords=StopFilter.makeStopSet(stopWordList);
}

public TokenStream tokenStream(String fieldName, Reader reader)
{
    TokenStream result = new StandardTokenizer(reader);
    result = new LowerCaseFilter(result);
    result = new StopFilter(result, stopWords);
    result = new PorterStemFilter(result);
    return result;
}

public static void main(String[] args)
{
    //好像英文的結束符號標點.,StandardAnalyzer不能識別
    String string = new String("我愛中國,我愛天津大學!I love China!Tianjin is a City");
    Analyzer analyzer = new DiyAnalyzer();
    TokenStream ts = analyzer.tokenStream("dummy", new StringReader(string));
    Token token;
    try
    {
      while ( (token = ts.next()) != null)
      {
        System.out.println(token.toString());
      }
    }
    catch (IOException ioe)
    {
      ioe.printStackTrace();
    }
}
}

可以看見其后的結果如下:

Token's (termText,startOffset,endOffset,type,positionIncrement) is:(愛,1,2,<CJK>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(中,2,3,<CJK>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(國,3,4,<CJK>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(愛,6,7,<CJK>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(天,7,8,<CJK>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(津,8,9,<CJK>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(大,9,10,<CJK>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(學,10,11,<CJK>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(i,12,13,<ALPHANUM>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(love,14,18,<ALPHANUM>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(china,19,24,<ALPHANUM>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(tianjin,25,32,<ALPHANUM>,1)
Token's (termText,startOffset,endOffset,type,positionIncrement) is:(citi,39,43,<ALPHANUM>,1)

到此為止這個簡單的但是功能強大的分詞器就寫完了,下面咱們可以嘗試寫一個功能更強大的分詞器.

如何DIY一個功能更加強大Analyzer

譬如你有詞典,然后你根據(jù)正向最大匹配法或者逆向最大匹配法寫了一個分詞方法,卻想在Lucene中應用,很簡單,你只要把他們包裝成Lucene的TokenStream就好了.下邊我以調用中科院寫的ICTCLAS接口為例,進行演示.你去中科院網(wǎng)站可以拿到此接口的free版本,誰叫你沒錢呢,有錢,你就可以購買了.哈哈
好,由于ICTCLAS進行分詞之后,在Java中,中間會以兩個空格隔開!too easy,我們直接使用繼承Lucene的WhiteSpaceTokenizer就好了.

所以TjuChineseTokenizer 看起來像是這樣.

public class TjuChineseTokenizer extends WhitespaceTokenizer
{
public TjuChineseTokenizer(Reader readerInput)
{
super(readerInput);
}
}

而TjuChineseAnalyzer看起來象是這樣

public final class TjuChineseAnalyzer
extends Analyzer
{
private Set stopWords;

/** An array containing some common English words that are not usually useful
    for searching. */
/*
     public static final String[] CHINESE_ENGLISH_STOP_WORDS =
      {
      "a", "an", "and", "are", "as", "at", "be", "but", "by",
      "for", "if", "in", "into", "is", "it",
      "no", "not", "of", "on", "or", "s", "such",
      "t", "that", "the", "their", "then", "there", "these",
      "they", "this", "to", "was", "will", "with",
      "我", "我們"
     };
   */
/** Builds an analyzer which removes words in ENGLISH_STOP_WORDS. */
public TjuChineseAnalyzer()
{
    stopWords = StopFilter.makeStopSet(StopWords.SMART_CHINESE_ENGLISH_STOP_WORDS);
}

/** Builds an analyzer which removes words in the provided array. */
//提供獨自的stopwords
public TjuChineseAnalyzer(String[] stopWords)
{
this.stopWords = StopFilter.makeStopSet(stopWords);
}

/** Filters LowerCaseTokenizer with StopFilter. */
public TokenStream tokenStream(String fieldName, Reader reader)
{
    try
    {
      ICTCLAS splitWord = new ICTCLAS();
      String inputString = FileIO.readerToString(reader);

      //分詞中間加入了空格
      String resultString = splitWord.paragraphProcess(inputString);
      System.out.println(resultString);
      TokenStream result = new TjuChineseTokenizer(new StringReader(resultString));

      result = new LowerCaseFilter(result);
      //使用stopWords進行過濾
     result = new StopFilter(result, stopWords);

//使用p-stemming算法進行過濾
result = new PorterStemFilter(result);

return result;

    }
    catch (IOException e)
    {
      System.out.println("轉換出錯");
      return null;
    }
}

public static void main(String[] args)
{
    String string = "我愛中國人民";
    Analyzer analyzer = new TjuChineseAnalyzer();
    TokenStream ts = analyzer.tokenStream("dummy", new StringReader(string));
    Token token;
    System.out.println("Tokens:");
    try
    {
      int n=0;
      while ( (token = ts.next()) != null)
      {
        System.out.println((n++)+"->"+token.toString());
      }
    }
    catch (IOException ioe)
    {
     ioe.printStackTrace();
    }
}
}
對于此程序的輸出接口可以看一下
0->Token's (termText,startOffset,endOffset,type,positionIncrement) is:(愛,3,4,word,1)
1->Token's (termText,startOffset,endOffset,type,positionIncrement) is:(中國,6,8,word,1)
2->Token's (termText,startOffset,endOffset,type,positionIncrement) is:(人民,10,12,word,1)