Java开源的11个中文分词器使用方法和分词效果对比

本文的目标有两个：

1、学会使用11大Java开源中文分词器

2、对比分析11大Java开源中文分词器的分词效果

本文给出了11大Java开源中文分词的使用方法以及分词结果对比代码，至于效果哪个好，那要用的人结合自己的应用场景自己来判断。

11大Java开源中文分词器，不同的分词器有不同的用法，定义的接口也不一样，我们先定义一个统一的接口：

/*** 获取文本的所有分词结果, 对比不同分词器结果* @author 杨尚川*/public interface WordSegmenter {/*** 获取文本的所有分词结果* @param text 文本* @return 所有的分词结果，去除重复*/default public Set<String> seg(String text) {return segMore(text).values().stream().collect(Collectors.toSet());}/*** 获取文本的所有分词结果* @param text 文本* @return 所有的分词结果，KEY 为分词器模式，VALUE 为分词器结果*/public Map<String, String> segMore(String text);}

从上面的定义我们知道，在Java中，同样的方法名称和参数，但是返回值不同，这种情况不可以使用重载。

这两个方法的区别在于返回值，每一个分词器都可能有多种分词模式，每种模式的分词结果都可能不相同，第一个方法忽略分词器模式，返回所有模式的所有不重复分词结果，第二个方法返回每一种分词器模式及其对应的分词结果。

在这里，需要注意的是我们使用了Java8中的新特性默认方法，并使用stream把一个map的value转换为不重复的集合。

下面我们利用这11大分词器来实现这个接口：
1、word分词器

@Overridepublic Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();for(SegmentationAlgorithm segmentationAlgorithm : SegmentationAlgorithm.values()){map.put(segmentationAlgorithm.getDes(), seg(text, segmentationAlgorithm));}return map;}private static String seg(String text, SegmentationAlgorithm segmentationAlgorithm) {StringBuilder result = new StringBuilder();for(Word word : WordSegmenter.segWithStopWords(text, segmentationAlgorithm)){result.append(word.getText()).append(" ");}return result.toString();}

2、Ansj分词器

@Overridepublic Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();StringBuilder result = new StringBuilder();for(Term term : BaseAnalysis.parse(text)){result.append(term.getName()).append(" ");}map.put("BaseAnalysis", result.toString());result.setLength(0);for(Term term : ToAnalysis.parse(text)){result.append(term.getName()).append(" ");}map.put("ToAnalysis", result.toString());result.setLength(0);for(Term term : NlpAnalysis.parse(text)){result.append(term.getName()).append(" ");}map.put("NlpAnalysis", result.toString());result.setLength(0);for(Term term : IndexAnalysis.parse(text)){result.append(term.getName()).append(" ");}map.put("IndexAnalysis", result.toString());return map;}

3、Stanford分词器

private static final StanfordCoreNLP CTB = new StanfordCoreNLP("StanfordCoreNLP-chinese-ctb");private static final StanfordCoreNLP PKU = new StanfordCoreNLP("StanfordCoreNLP-chinese-pku");private static final PrintStream NULL_PRINT_STREAM = new PrintStream(new NullOutputStream(), false);public Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();map.put("Stanford Beijing University segmentation", seg(PKU, text));map.put("Stanford Chinese Treebank segmentation", seg(CTB, text));return map;}private static String seg(StanfordCoreNLP stanfordCoreNLP, String text){PrintStream err = System.err;System.setErr(NULL_PRINT_STREAM);Annotation document = new Annotation(text);stanfordCoreNLP.annotate(document);List<CoreMap> sentences = document.get(CoreAnnotations.SentencesAnnotation.class);StringBuilder result = new StringBuilder();for(CoreMap sentence: sentences) {for (CoreLabel token: sentence.get(CoreAnnotations.TokensAnnotation.class)) {String word = token.get(CoreAnnotations.TextAnnotation.class);;result.append(word).append(" ");}}System.setErr(err);return result.toString();}

4、FudanNLP分词器

private static CWSTagger tagger = null;static{try{tagger = new CWSTagger("lib/fudannlp_seg.m");tagger.setEnFilter(true);}catch(Exception e){e.printStackTrace();}}@Overridepublic Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();map.put("FudanNLP", tagger.tag(text));return map;}

5、Jieba分词器

private static final JiebaSegmenter JIEBA_SEGMENTER = new JiebaSegmenter();@Overridepublic Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();map.put("INDEX", seg(text, SegMode.INDEX));map.put("SEARCH", seg(text, SegMode.SEARCH));return map;}private static String seg(String text, SegMode segMode) {StringBuilder result = new StringBuilder();                for(SegToken token : JIEBA_SEGMENTER.process(text, segMode)){result.append(token.word.getToken()).append(" ");}return result.toString(); }

6、Jcseg分词器

private static final JcsegTaskConfig CONFIG = new JcsegTaskConfig();private static final ADictionary DIC = DictionaryFactory.createDefaultDictionary(CONFIG);static {CONFIG.setLoadCJKSyn(false);CONFIG.setLoadCJKPinyin(false);}@Overridepublic Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();map.put("复杂模式", segText(text, JcsegTaskConfig.COMPLEX_MODE));map.put("简易模式", segText(text, JcsegTaskConfig.SIMPLE_MODE));return map;}private String segText(String text, int segMode) {StringBuilder result = new StringBuilder();        try {ISegment seg = SegmentFactory.createJcseg(segMode, new Object[]{new StringReader(text), CONFIG, DIC});IWord word = null;while((word=seg.next())!=null) {         result.append(word.getValue()).append(" ");}} catch (Exception ex) {throw new RuntimeException(ex);}return result.toString();}

7、MMSeg4j分词器

private static final Dictionary DIC = Dictionary.getInstance();private static final SimpleSeg SIMPLE_SEG = new SimpleSeg(DIC);private static final ComplexSeg COMPLEX_SEG = new ComplexSeg(DIC);private static final MaxWordSeg MAX_WORD_SEG = new MaxWordSeg(DIC);@Overridepublic Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();map.put(SIMPLE_SEG.getClass().getSimpleName(), segText(text, SIMPLE_SEG));map.put(COMPLEX_SEG.getClass().getSimpleName(), segText(text, COMPLEX_SEG));map.put(MAX_WORD_SEG.getClass().getSimpleName(), segText(text, MAX_WORD_SEG));return map;}private String segText(String text, Seg seg) {StringBuilder result = new StringBuilder();MMSeg mmSeg = new MMSeg(new StringReader(text), seg);        try {Word word = null;while((word=mmSeg.next())!=null) {       result.append(word.getString()).append(" ");}} catch (IOException ex) {throw new RuntimeException(ex);}return result.toString();}

8、IKAnalyzer分词器

@Overridepublic Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();map.put("智能切分", segText(text, true));map.put("细粒度切分", segText(text, false));return map;}private String segText(String text, boolean useSmart) {StringBuilder result = new StringBuilder();IKSegmenter ik = new IKSegmenter(new StringReader(text), useSmart);        try {Lexeme word = null;while((word=ik.next())!=null) {          result.append(word.getLexemeText()).append(" ");}} catch (IOException ex) {throw new RuntimeException(ex);}return result.toString();}

9、Paoding分词器

private static final PaodingAnalyzer ANALYZER = new PaodingAnalyzer();@Overridepublic Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();map.put("MOST_WORDS_MODE", seg(text, PaodingAnalyzer.MOST_WORDS_MODE));map.put("MAX_WORD_LENGTH_MODE", seg(text, PaodingAnalyzer.MAX_WORD_LENGTH_MODE));return map;}private static String seg(String text, int mode){ANALYZER.setMode(mode);StringBuilder result = new StringBuilder();try {Token reusableToken = new Token();TokenStream stream = ANALYZER.tokenStream("", new StringReader(text));Token token = null;while((token = stream.next(reusableToken)) != null){result.append(token.term()).append(" ");}} catch (Exception ex) {throw new RuntimeException(ex);}return result.toString();          }

10、smartcn分词器

private static final SmartChineseAnalyzer SMART_CHINESE_ANALYZER = new SmartChineseAnalyzer();@Overridepublic Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();map.put("smartcn", segText(text));return map;}private static String segText(String text) {StringBuilder result = new StringBuilder();try {TokenStream tokenStream = SMART_CHINESE_ANALYZER.tokenStream("text", new StringReader(text));tokenStream.reset();while (tokenStream.incrementToken()){CharTermAttribute charTermAttribute = tokenStream.getAttribute(CharTermAttribute.class);result.append(charTermAttribute.toString()).append(" ");}tokenStream.close();}catch (Exception e){e.printStackTrace();}return result.toString();}

11、HanLP分词器

private static final Segment N_SHORT_SEGMENT = new NShortSegment().enableCustomDictionary(false).enablePlaceRecognize(true).enableOrganizationRecognize(true);private static final Segment DIJKSTRA_SEGMENT = new DijkstraSegment().enableCustomDictionary(false).enablePlaceRecognize(true).enableOrganizationRecognize(true);@Overridepublic Map<String, String> segMore(String text) {Map<String, String> map = new HashMap<>();map.put("标准分词", standard(text));map.put("NLP分词", nlp(text));map.put("索引分词", index(text));map.put("N-最短路径分词", nShort(text));map.put("最短路径分词", shortest(text));map.put("极速词典分词", speed(text));return map;}private static String standard(String text) {StringBuilder result = new StringBuilder();StandardTokenizer.segment(text).forEach(term->result.append(term.word).append(" "));return result.toString();}private static String nlp(String text) {StringBuilder result = new StringBuilder();NLPTokenizer.segment(text).forEach(term->result.append(term.word).append(" "));return result.toString();}private static String index(String text) {StringBuilder result = new StringBuilder();IndexTokenizer.segment(text).forEach(term->result.append(term.word).append(" "));return result.toString();}private static String speed(String text) {StringBuilder result = new StringBuilder();SpeedTokenizer.segment(text).forEach(term->result.append(term.word).append(" "));return result.toString();}private static String nShort(String text) {StringBuilder result = new StringBuilder();N_SHORT_SEGMENT.seg(text).forEach(term->result.append(term.word).append(" "));return result.toString();}private static String shortest(String text) {StringBuilder result = new StringBuilder();DIJKSTRA_SEGMENT.seg(text).forEach(term->result.append(term.word).append(" "));return result.toString();}

现在我们已经实现了本文的第一个目的：学会使用11大Java开源中文分词器。

最后我们来实现本文的第二个目的：对比分析11大Java开源中文分词器的分词效果，程序如下：

public static Map<String, Set<String>> contrast(String text){Map<String, Set<String>> map = new LinkedHashMap<>();map.put("word分词器", new WordEvaluation().seg(text));map.put("Stanford分词器", new StanfordEvaluation().seg(text));map.put("Ansj分词器", new AnsjEvaluation().seg(text));map.put("HanLP分词器", new HanLPEvaluation().seg(text));map.put("FudanNLP分词器", new FudanNLPEvaluation().seg(text));map.put("Jieba分词器", new JiebaEvaluation().seg(text));map.put("Jcseg分词器", new JcsegEvaluation().seg(text));map.put("MMSeg4j分词器", new MMSeg4jEvaluation().seg(text));map.put("IKAnalyzer分词器", new IKAnalyzerEvaluation().seg(text));map.put("smartcn分词器", new SmartCNEvaluation().seg(text));return map;}public static Map<String, Map<String, String>> contrastMore(String text){Map<String, Map<String, String>> map = new LinkedHashMap<>();map.put("word分词器", new WordEvaluation().segMore(text));map.put("Stanford分词器", new StanfordEvaluation().segMore(text));map.put("Ansj分词器", new AnsjEvaluation().segMore(text));map.put("HanLP分词器", new HanLPEvaluation().segMore(text));map.put("FudanNLP分词器", new FudanNLPEvaluation().segMore(text));map.put("Jieba分词器", new JiebaEvaluation().segMore(text));map.put("Jcseg分词器", new JcsegEvaluation().segMore(text));map.put("MMSeg4j分词器", new MMSeg4jEvaluation().segMore(text));map.put("IKAnalyzer分词器", new IKAnalyzerEvaluation().segMore(text));map.put("smartcn分词器", new SmartCNEvaluation().segMore(text));return map;}public static void show(Map<String, Set<String>> map){map.keySet().forEach(k -> {System.out.println(k + " 的分词结果：");AtomicInteger i = new AtomicInteger();map.get(k).forEach(v -> {System.out.println("\t" + i.incrementAndGet() + " 、" + v);});});}public static void showMore(Map<String, Map<String, String>> map){map.keySet().forEach(k->{System.out.println(k + " 的分词结果：");AtomicInteger i = new AtomicInteger();map.get(k).keySet().forEach(a -> {System.out.println("\t" + i.incrementAndGet()+ " 、【"   + a + "】\t" + map.get(k).get(a));});});}public static void main(String[] args) {show(contrast("我爱楚离陌"));showMore(contrastMore("我爱楚离陌"));}

运行结果如下：

word分词器的分词结果：

1 、我 爱 楚离陌

Stanford分词器的分词结果：

1 、我 爱 楚 离陌 2 、我 爱 楚离陌

Ansj分词器的分词结果：

1 、我 爱 楚离 陌 2 、我 爱 楚 离 陌

HanLP分词器的分词结果：

1 、我 爱 楚 离 陌

smartcn分词器的分词结果：

1 、我 爱 楚 离 陌

FudanNLP分词器的分词结果：

1 、我 爱楚离陌

Jieba分词器的分词结果：

1 、我爱楚 离 陌

Jcseg分词器的分词结果：

1 、我 爱 楚 离 陌

MMSeg4j分词器的分词结果：

1 、我爱 楚 离 陌

IKAnalyzer分词器的分词结果：

1 、我 爱 楚 离 陌