10⼤Java开源中⽂分词器的使⽤⽅法和分词效果对⽐
本⽂的⽬标有两个：
1、学会使⽤10⼤Java开源中⽂分词器
2、对⽐分析10 ⼤Java开源中⽂分词器的分词效果
本⽂给出了10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。
10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().Set());
}
/**
* 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10⼤分词器来实现这个接⼝：
1、word分词器
@Override
public Map<String, String> segMore(String text) {
Map<String, String> map = new HashMap<>();
for(SegmentationAlgorithm segmentationAlgorithm : SegmentationAlgorithm.values()){
map.Des(), 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.Text()).append(" ");
}
String();
}
2、Ansj分词器
@Override
public Map<String, String> segMore(String text) {
Map<String, String> map = new HashMap<>();
StringBuilder result = new StringBuilder();
for(Term term : BaseAnalysis.parse(text)){
result.Name()).append(" ");
}
map.put("BaseAnalysis", String());
result.setLength(0);
for(Term term : ToAnalysis.parse(text)){
result.Name()).append(" ");
}
map.put("ToAnalysis", String());
result.setLength(0);
for(Term term : NlpAnalysis.parse(text)){
result.Name()).append(" ");
}
map.put("NlpAnalysis", String());
result.setLength(0);
for(Term term : IndexAnalysis.parse(text)){
result.Name()).append(" ");
}
map.put("IndexAnalysis", String());
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.setErr(NULL_PRINT_STREAM);
Annotation document = new Annotation(text);
stanfordCoreNLP.annotate(document);
List<CoreMap> sentences = (CoreAnnotations.SentencesAnnotation.class);
StringBuilder result = new StringBuilder();
for(CoreMap sentence: sentences) {
for (CoreLabel token: (CoreAnnotations.TokensAnnotation.class)) {
String word = (CoreAnnotations.TextAnnotation.class);;
result.append(word).append(" ");
}
}
System.setErr(err);
String();
}
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();
}
}
@Override
public 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(); @Override
public 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()).append(" ");
}
String();
}
6、Jcseg分词器
private static final JcsegTaskConfig CONFIG = new JcsegTaskConfig();
private static final ADictionary DIC = ateDefaultDictionary(CONFIG);
try catch的使用方法static {
CONFIG.setLoadCJKSyn(false);
CONFIG.setLoadCJKPinyin(false);
}
@Override
public 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 = ateJcseg(segMode, new Object[]{new StringReader(text), CONFIG, DIC});    IWord word = null;
while((())!=null) {
result.Value()).append(" ");
}
} catch (Exception ex) {
throw new RuntimeException(ex);
}
String();
}
7、MMSeg4j分词器
private static final Dictionary DIC = Instance();
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);
@Override
public Map<String, String> segMore(String text) {
Map<String, String> map = new HashMap<>();
map.put(Class().getSimpleName(), segText(text, SIMPLE_SEG));
map.put(Class().getSimpleName(), segText(text, COMPLEX_SEG));
map.put(MAX_Class().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((())!=null) {
result.String()).append(" ");
}
} catch (IOException ex) {
throw new RuntimeException(ex);
}
String();
}
8、IKAnalyzer分词器
@Override
public 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((())!=null) {
result.LexemeText()).append(" ");
}
} catch (IOException ex) {
throw new RuntimeException(ex);
}
String();
}
9、Paoding分词器
private static final PaodingAnalyzer ANALYZER = new PaodingAnalyzer();
@Override
public 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 = kenStream("", new StringReader(text));
Token token = null;
while((token = (reusableToken)) != null){
result.()).append(" ");
}
} catch (Exception ex) {
throw new RuntimeException(ex);
}
String();
}
10、smartcn分词器

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