Lucene文本解析器实现 把一段文本信息拆分成多个分词，我们都知道搜索引擎是通过分词检索的，文本解析器的好坏直接决定了搜索的精度和搜索的速度。
1.简单的Demo
private static final String[] examples = { "The quick brown 1234 fox jumped over the lazy dog!","XY&Z 15.6 Corporation - xyz@example.com", "北京市北京大学" };
private static final Analyzer[] ANALYZERS = new Analyzer[] {
new WhitespaceAnalyzer(), new SimpleAnalyzer(), new StopAnalyzer(), new StandardAnalyzer(), new CJKAnalyzer(), new SmartChineseAnalyzer() };
//空格符拆分 非字母拆分 非字母拆分去掉停词 Unicode文本分割 日韩文分割 简体中文分割
@Test
public void testAnalyzer() throws IOException {
for (int i = 0; i < ANALYZERS.length; i++) {
String simpleName = ANALYZERS[i].getClass().getSimpleName();
for (int j = 0; j < examples.length; j++) {
//TokenStream是分析处理组件中的一种中间数据格式，它从一个reader中获取文本， 分词器Tokenizer和过滤器TokenFilter继承自TokenStream
TokenStream contents = ANALYZERS[i].tokenStream("contents", new StringReader(examples[j]));
//添加多个Attribute，从而可以了解到分词之后详细的词元信息 ，OffsetAttribute 表示token的首字母和尾字母在原文本中的位置
OffsetAttribute offsetAttribute = contents.addAttribute(OffsetAttribute.class);
TypeAttribute typeAttribute = contents.addAttribute(TypeAttribute.class); //TypeAttribute 表示token的词汇类型信息，默认值为word
contents.reset();
System.out.println(" " + simpleName + " analyzing : " + examples[j]);
while (contents.incrementToken()) {
String s1 = offsetAttribute.toString();
int i1 = offsetAttribute.startOffset();// 起始偏移量
int i2 = offsetAttribute.endOffset(); // 结束偏移量
System.out.println(" " + s1 + "[" + i1 + "," + i2 + ":" + typeAttribute.type() + "]" + " ");
}
contents.end();
contents.close(); //调用incrementToken()结束迭代之后，调用end()和close()方法，其中end()可以唤醒当前TokenStream的处理器去做一些收尾工作，close()可以关闭TokenStream和Analyzer去释放在分析过程中使用的资源。
System.out.println();
}
}
}
}
2. 了解tokenStream的Attribute
tokenStream()方法之后，添加多个Attribute，可以了解到分词之后详细的词元信息，比如CharTermAttribute用于保存词元的内容，TypeAttribute用于保存词元的类型。
CharTermAttribute 表示token本身的内容
PositionIncrementAttribute 表示当前token相对于前一个token的相对位置，也就是相隔的词语数量（例如“text for attribute”，
text和attribute之间的getPositionIncrement为2），如果两者之间没有停用词，那么该值被置为默认值1
OffsetAttribute 表示token的首字母和尾字母在原文本中的位置
TypeAttribute 表示token的词汇类型信息，默认值为word，
其它值有<ALPHANUM> <APOSTROPHE> <ACRONYM> <COMPANY> <EMAIL> <HOST> <NUM> <CJ> <ACRONYM_DEP>
FlagsAttribute 与TypeAttribute类似，假设你需要给token添加额外的信息，而且希望该信息可以通过分析链，那么就可以通过flags去传递
PayloadAttribute 在每个索引位置都存储了payload（关键信息），当使用基于Payload的查询时，该信息在评分中非常有用
@Test
public void testAttribute() throws IOException {
Analyzer analyzer = new StandardAnalyzer();
String input = "This is a test text for attribute! Just add-some word.";
TokenStream tokenStream = analyzer.tokenStream("text", new StringReader(input)); CharTermAttribute charTermAttribute = tokenStream.addAttribute(CharTermAttribute.class);
PositionIncrementAttribute positionIncrementAttribute = tokenStream.addAttribute(PositionIncrementAttribute.class);
OffsetAttribute offsetAttribute = tokenStream.addAttribute(OffsetAttribute.class);
TypeAttribute typeAttribute = tokenStream.addAttribute(TypeAttribute.class);
PayloadAttribute payloadAttribute = tokenStream.addAttribute(PayloadAttribute.class);
payloadAttribute.setPayload(new BytesRef("Just")); tokenStream.reset();
while (tokenStream.incrementToken()) {
System.out.print(
"[" + charTermAttribute
+ " increment:" + positionIncrementAttribute.getPositionIncrement()
+ " start:" + offsetAttribute.startOffset()
+ " end:" + offsetAttribute.endOffset()
+ " type:"+ typeAttribute.type()
+ " payload:" + payloadAttribute.getPayload() + "]\n");
} tokenStream.end();
tokenStream.close();
}
3.Lucene 的分词器Tokenizer和过滤器TokenFilter
一个分析器由一个分词器和多个过滤器组成，分词器接受reader数据转换成 TokenStream，TokenFilter主要用于TokenStream的过滤操作，用来处理Tokenizer或者上一个TokenFilter处理后的结果，如果是对现有分词器进行扩展或修改。
自定义TokenFilter需要实现incrementToken()抽象函数，
public class TestTokenFilter {
@Test
public void test() throws IOException {
String text = "Hi, Dr Wang, Mr Liu asks if you stay with Mrs Liu yesterday!";
Analyzer analyzer = new WhitespaceAnalyzer();
CourtesyTitleFilter filter = new CourtesyTitleFilter(analyzer.tokenStream("text", text));
CharTermAttribute charTermAttribute = filter.addAttribute(CharTermAttribute.class);
filter.reset();
while (filter.incrementToken()) {
System.out.print(charTermAttribute + " ");
}
}
}
/**
* 自定义词扩展过滤器
*/
class CourtesyTitleFilter extends TokenFilter {
Map<String, String> courtesyTitleMap = new HashMap<>();
private CharTermAttribute termAttribute;
protected CourtesyTitleFilter(TokenStream input) {
super(input);
termAttribute = addAttribute(CharTermAttribute.class);
courtesyTitleMap.put("Dr", "doctor");
courtesyTitleMap.put("Mr", "mister");
courtesyTitleMap.put("Mrs", "miss");
}
@Override
public final boolean incrementToken() throws IOException {
if (!input.incrementToken()) {
return false;
}
String small = termAttribute.toString();
if (courtesyTitleMap.containsKey(small)) {
termAttribute.setEmpty().append(courtesyTitleMap.get(small));
}
return true;
}
}
输出结果如下
Hi, doctor Wang, mister Liu asks if you stay with miss Liu yesterday!
4.自定义Analyzer实现扩展停用词
class StopAnalyzerExtend extends Analyzer {
private CharArraySet stopWordSet;//停止词词典
public CharArraySet getStopWordSet() {
return this.stopWordSet;
}
public void setStopWordSet(CharArraySet stopWordSet) {
this.stopWordSet = stopWordSet;
}
public StopAnalyzerExtend() {
super();
setStopWordSet(StopAnalyzer.ENGLISH_STOP_WORDS_SET);
}
/**
* @param stops 需要扩展的停止词
*/
public StopAnalyzerExtend(List<String> stops) {
this();
/**如果直接为stopWordSet赋值的话，会报如下异常，这是因为在StopAnalyzer中有ENGLISH_STOP_WORDS_SET = CharArraySet.unmodifiableSet(stopSet);
* ENGLISH_STOP_WORDS_SET 被设置为不可更改的set集合
*/
//stopWordSet = getStopWordSet();
stopWordSet = CharArraySet.copy(getStopWordSet());
stopWordSet.addAll(StopFilter.makeStopSet(stops));
}
@Override
protected TokenStreamComponents createComponents(String fieldName) {
Tokenizer source = new LowerCaseTokenizer();
return new TokenStreamComponents(source, new StopFilter(source, stopWordSet));
}
public static void main(String[] args) throws IOException {
ArrayList<String> strings = new ArrayList<String>() {{
add("小鬼子");
add("美国佬");
}};
Analyzer analyzer = new StopAnalyzerExtend(strings);
String content = "小鬼子 and 美国佬 are playing together!";
TokenStream tokenStream = analyzer.tokenStream("myfield", content);
tokenStream.reset();
CharTermAttribute charTermAttribute = tokenStream.addAttribute(CharTermAttribute.class);
while (tokenStream.incrementToken()) {
// 已经过滤掉自定义停用词
// 输出：playing together
System.out.println(charTermAttribute.toString());
}
tokenStream.end();
tokenStream.close();
}
}
5.自定义Analyzer实现字长过滤
class LongFilterAnalyzer extends Analyzer {
private int len;
public int getLen() {
return this.len;
}
public void setLen(int len) {
this.len = len;
}
public LongFilterAnalyzer() {
super();
}
public LongFilterAnalyzer(int len) {
super();
setLen(len);
}
@Override
protected TokenStreamComponents createComponents(String fieldName) {
final Tokenizer source = new WhitespaceTokenizer();
//过滤掉长度<len，并且>20的token
TokenStream tokenStream = new LengthFilter(source, len, 20);
return new TokenStreamComponents(source, tokenStream);
}
public static void main(String[] args) {
//把长度小于2的过滤掉，开区间
Analyzer analyzer = new LongFilterAnalyzer(2);
String words = "I am a java coder! Testingtestingtesting!";
TokenStream stream = analyzer.tokenStream("myfield", words);
try {
stream.reset();
CharTermAttribute offsetAtt = stream.addAttribute(CharTermAttribute.class);
while (stream.incrementToken()) {
System.out.println(offsetAtt.toString());
}
stream.end();
stream.close();
} catch (IOException e) {
}
}
}
长度小于两个字符的文本都被过滤掉了。
6.PerFieldAnalyzerWrapper 处理不同的Field使用不同的Analyzer 。PerFieldAnalyzerWrapper可以像其它的Analyzer一样使用，包括索引和查询分析
@Test
public void testPerFieldAnalyzerWrapper() throws IOException, ParseException {
Map<String, Analyzer> fields = new HashMap<>();
fields.put("partnum", new KeywordAnalyzer());
// 对于其他的域，默认使用SimpleAnalyzer分析器，对于指定的域partnum使用KeywordAnalyzer
PerFieldAnalyzerWrapper perFieldAnalyzerWrapper = new PerFieldAnalyzerWrapper(new SimpleAnalyzer(), fields); Directory directory = new RAMDirectory();
IndexWriterConfig indexWriterConfig = new IndexWriterConfig(perFieldAnalyzerWrapper);
IndexWriter indexWriter = new IndexWriter(directory, indexWriterConfig);
Document document = new Document();
FieldType fieldType = new FieldType();
fieldType.setStored(true);
fieldType.setIndexOptions(IndexOptions.DOCS_AND_FREQS);
document.add(new Field("partnum", "Q36", fieldType));
document.add(new Field("description", "Illidium Space Modulator", fieldType));
indexWriter.addDocument(document);
indexWriter.close(); IndexSearcher indexSearcher = new IndexSearcher(DirectoryReader.open(directory));
// 直接使用TermQuery是可以检索到的
TopDocs search = indexSearcher.search(new TermQuery(new Term("partnum", "Q36")), 10);
Assert.assertEquals(1, search.totalHits);
// 如果使用QueryParser，那么必须要使用PerFieldAnalyzerWrapper，否则如下所示，是检索不到的
Query description = new QueryParser("description", new SimpleAnalyzer()).parse("partnum:Q36 AND SPACE");
search = indexSearcher.search(description, 10);
Assert.assertEquals(0, search.totalHits);
System.out.println("SimpleAnalyzer :" + description.toString());// +partnum:q
// +description:space，原因是SimpleAnalyzer会剥离非字母字符并将字母小写化
// 使用PerFieldAnalyzerWrapper可以检索到
// partnum:Q36 AND SPACE表示在partnum中出现Q36，在description中出现SPACE
description = new QueryParser("description", perFieldAnalyzerWrapper).parse("partnum:Q36 AND SPACE");
search = indexSearcher.search(description, 10);
Assert.assertEquals(1, search.totalHits);
System.out.println("(SimpleAnalyzer,KeywordAnalyzer) :" + description.toString());// +partnum:Q36 +description:space
}
参考 ： http://www.codepub.cn/2016/05/23/Lucene-6-0-in-action-4-The-text-analyzer/