lucene3 中文IKAnalyzer分词例子

import java.io.IOException;
import java.io.StringReader;
import java.util.Iterator;

import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.Field;
import org.apache.lucene.index.IndexWriter;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.ScoreDoc;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.store.Directory;
import org.apache.lucene.store.RAMDirectory;
import org.apache.lucene.util.AttributeImpl;
import org.wltea.analyzer.lucene.IKAnalyzer;
import org.wltea.analyzer.lucene.IKQueryParser;
import org.wltea.analyzer.lucene.IKSimilarity;
/**
 * 采用IKAanlyzer分词器查询
 * @author admin
 *
 */
public class IKAnalyzerSearchWord {
	private static String fieldName = "text";
	public  static void searchWord(String field ,String keyword) {
		if(null!=field&&!"".equals(field)){
			fieldName = field;
		}
		String text = "IK Analyzer是一个开源的，基于java语言开发的轻量级的中文分词工具包。从2006年12月推出1.0版开始， " +
				"IKAnalyzer已经推出了3个大版本。最初，它是以开源项目Luence为应用主体的，结合词典分词和文法分析算法的中文分词组件。" +
				"新版本的IK Analyzer 3.0则发展为面向Java的公用分词组件，独立于Lucene项目，同时提供了对Lucene的默认优化实现。 ";   
		Analyzer analyzer = new IKAnalyzer();
		StringReader reader = new StringReader(text);   

		long startTime = System.currentTimeMillis();    //开始时间   
		TokenStream ts = analyzer.tokenStream("*", reader);   
		Iterator it = ts.getAttributeImplsIterator();
		while(it.hasNext()){
			System.out.println((AttributeImpl)it.next());
		}
		System.out.println("");   

		long endTime = System.currentTimeMillis();  //结束时间   
		System.out.println("IK分词耗时" + new Float((endTime - startTime)) / 1000 + "秒!"); 
		Directory dir = null;
		IndexWriter writer = null;
		IndexSearcher searcher = null;
		try {
			dir = new RAMDirectory();
			writer = new IndexWriter(dir, analyzer, true,
					IndexWriter.MaxFieldLength.LIMITED);
			System.out.println(IndexWriter.MaxFieldLength.LIMITED);
			Document doc = new Document();
			doc.add(new Field(fieldName, text, Field.Store.YES,
					Field.Index.ANALYZED));
			writer.addDocument(doc);
			writer.close();
			//在索引其中使用IKSimilarity相似评估度
			searcher = new IndexSearcher(dir);
			searcher.setSimilarity(new IKSimilarity());
			Query query = IKQueryParser.parse(fieldName, keyword);
			TopDocs topDocs = searcher.search(query, 5);
			System.out.println("命中："+topDocs.totalHits);
			ScoreDoc[] scoreDocs = topDocs.scoreDocs;
			for (int i = 0; i < scoreDocs.length; i++) {
				Document targetDoc = searcher.doc(scoreDocs[i].doc);
				System.out.println("內容："+targetDoc.toString());
			}
		} catch (Exception e) {
			System.out.println(e);
		}finally{
			try {
				searcher.close();
			} catch (IOException e) {
				e.printStackTrace();
			}
			try {
				dir.close();
			} catch (IOException e) {
				e.printStackTrace();
			}
		}
	}
	public static void main(String[] args) {
		long a = System.currentTimeMillis();
		IKAnalyzerSearchWord.searchWord("","中文分词工具包");
		System.out.println(System.currentTimeMillis()-a);
	}
}