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GPL-3.0

##Java分布式中文分词组件 - word分词

###word分词是一个Java实现的分布式的中文分词组件,提供了多种基于词典的分词算法,并利用ngram模型来消除歧义。能准确识别英文、数字,以及日期、时间等数量词,能识别人名、地名、组织机构名等未登录词。同时提供了Lucene、Solr、ElasticSearch插件。

###下载地址

###word 1.0 API

###word 1.1 API

###Maven依赖:

在pom.xml中指定dependency,可用版本有1.0和1.1:

<dependencies>
	<dependency>
		<groupId>org.apdplat</groupId>
		<artifactId>word</artifactId>
		<version>1.1</version>
	</dependency>
</dependencies>

###分词使用方法:

1、快速体验
运行项目根目录下的脚本demo-word.bat可以快速体验分词效果
用法: command [text] [input] [output]
命令command的可选值为:demo、text、file
demo
text 杨尚川是APDPlat应用级产品开发平台的作者
file d:/text.txt d:/word.txt
exit

2、对文本进行分词
移除停用词:List<Word> words = WordSegmenter.seg("杨尚川是APDPlat应用级产品开发平台的作者");
保留停用词:List<Word> words = WordSegmenter.segWithStopWords("杨尚川是APDPlat应用级产品开发平台的作者");
			System.out.println(words);

输出:
移除停用词:[杨尚川, apdplat, 应用级, 产品, 开发平台, 作者]
保留停用词:[杨尚川, 是, apdplat, 应用级, 产品, 开发平台, 的, 作者]

3、对文件进行分词
String input = "d:/text.txt";
String output = "d:/word.txt";
移除停用词:WordSegmenter.seg(new File(input), new File(output));
保留停用词:WordSegmenter.segWithStopWords(new File(input), new File(output));

4、自定义配置文件
默认配置文件为类路径下的word.conf,打包在word-x.x.jar中
自定义配置文件为类路径下的word.local.conf,需要用户自己提供
如果自定义配置和默认配置相同,自定义配置会覆盖默认配置
配置文件编码为UTF-8
	
5、自定义用户词库
自定义用户词库为一个或多个文件夹或文件,可以使用绝对路径或相对路径
用户词库由多个词典文件组成,文件编码为UTF-8
词典文件的格式为文本文件,一行代表一个词
可以通过系统属性或配置文件的方式来指定路径,多个路径之间用逗号分隔开
类路径下的词典文件,需要在相对路径前加入前缀classpath:
	
指定方式有三种:
	指定方式一,编程指定(高优先级):
		WordConfTools.set("dic.path", "classpath:dic.txt,d:/custom_dic");
		DictionaryFactory.reload();//更改词典路径之后,重新加载词典
	指定方式二,Java虚拟机启动参数(中优先级):
		java -Ddic.path=classpath:dic.txt,d:/custom_dic
	指定方式三,配置文件指定(低优先级):
		使用类路径下的文件word.local.conf来指定配置信息
		dic.path=classpath:dic.txt,d:/custom_dic

如未指定,则默认使用类路径下的dic.txt词典文件

6、自定义停用词词库
使用方式和自定义用户词库类似,配置项为:
stopwords.path=classpath:stopwords.txt,d:/custom_stopwords_dic
	
7、自动检测词库变化
可以自动检测自定义用户词库和自定义停用词词库的变化
包含类路径下的文件和文件夹、非类路径下的绝对路径和相对路径
如:
classpath:dic.txt,classpath:custom_dic_dir,
d:/dic_more.txt,d:/DIC_DIR,D:/DIC2_DIR,my_dic_dir,my_dic_file.txt

classpath:stopwords.txt,classpath:custom_stopwords_dic_dir,
d:/stopwords_more.txt,d:/STOPWORDS_DIR,d:/STOPWORDS2_DIR,stopwords_dir,remove.txt

8、显式指定分词算法
对文本进行分词时,可显式指定特定的分词算法,如:
WordSegmenter.seg("APDPlat应用级产品开发平台", SegmentationAlgorithm.BidirectionalMaximumMatching);

SegmentationAlgorithm的可选类型为:	 
正向最大匹配算法:MaximumMatching
逆向最大匹配算法:ReverseMaximumMatching
正向最小匹配算法:MinimumMatching
逆向最小匹配算法:ReverseMinimumMatching
双向最大匹配算法:BidirectionalMaximumMatching
双向最小匹配算法:BidirectionalMinimumMatching
双向最大最小匹配算法:BidirectionalMaximumMinimumMatching
全切分算法:FullSegmentation

9、分词效果评估
运行项目根目录下的脚本evaluation.bat可以对分词效果进行评估
评估采用的测试文本有253 3709行,共2837 4490个字符
评估结果位于target/evaluation目录下:
corpus-text.txt为分好词的人工标注文本,词之间以空格分隔
test-text.txt为测试文本,是把corpus-text.txt以标点符号分隔为多行的结果
standard-text.txt为测试文本对应的人工标注文本,作为分词是否正确的标准
result-text-***.txt,***为各种分词算法名称,这是word分词结果
perfect-result-***.txt,***为各种分词算法名称,这是分词结果和人工标注标准完全一致的文本
wrong-result-***.txt,***为各种分词算法名称,这是分词结果和人工标注标准不一致的文本

10、分布式中文分词器
1、在自定义配置文件word.conf或word.local.conf中指定所有的配置项*.path使用HTTP资源,同时指定配置项redis.*
2、配置并启动提供HTTP资源的web服务器,将项目:https://github.com/ysc/word_web部署到tomcat
3、配置并启动redis服务器

###分词算法效果评估:

1:word分词 全切分算法:
分词速度:74.09025 字符/毫秒
行数完美率:58.79%  行数错误率:41.2%  总的行数:2533709  完美行数:1489713  错误行数:1043996
字数完美率:49.53% 字数错误率:50.46% 总的字数:28374490 完美字数:14054431 错误字数:14320059

2:word分词 双向最大最小匹配算法:
分词速度:321.05466 字符/毫秒
行数完美率:55.31%  行数错误率:44.68%  总的行数:2533709  完美行数:1401582  错误行数:1132127
字数完美率:45.83% 字数错误率:54.16% 总的字数:28374490 完美字数:13005696 错误字数:15368794

3:word分词 双向最大匹配算法:
分词速度:505.47778 字符/毫秒
行数完美率:52.01%  行数错误率:47.98%  总的行数:2533709  完美行数:1317801  错误行数:1215908
字数完美率:42.42% 字数错误率:57.57% 总的字数:28374490 完美字数:12038414 错误字数:16336076

4:word分词 双向最小匹配算法:
分词速度:699.2235 字符/毫秒
行数完美率:46.76%  行数错误率:53.23%  总的行数:2533709  完美行数:1185013  错误行数:1348696
字数完美率:36.52% 字数错误率:63.47% 总的字数:28374490 完美字数:10365168 错误字数:18009322

5:word分词 逆向最大匹配算法:
分词速度:1161.7462 字符/毫秒
行数完美率:46.72%  行数错误率:53.27%  总的行数:2533709  完美行数:1183913  错误行数:1349796
字数完美率:36.67% 字数错误率:63.32% 总的字数:28374490 完美字数:10407342 错误字数:17967148

6:word分词 正向最大匹配算法:
分词速度:1212.7405 字符/毫秒
行数完美率:46.66%  行数错误率:53.33%  总的行数:2533709  完美行数:1182351  错误行数:1351358
字数完美率:36.73% 字数错误率:63.26% 总的字数:28374490 完美字数:10422209 错误字数:17952281

7:word分词 逆向最小匹配算法:
分词速度:2134.7043 字符/毫秒
行数完美率:41.78%  行数错误率:58.21%  总的行数:2533709  完美行数:1058606  错误行数:1475103
字数完美率:31.68% 字数错误率:68.31% 总的字数:28374490 完美字数:8989797 错误字数:19384693

8:word分词 正向最小匹配算法:
分词速度:2237.03 字符/毫秒
行数完美率:36.85%  行数错误率:63.14%  总的行数:2533709  完美行数:933769  错误行数:1599940
字数完美率:26.85% 字数错误率:73.14% 总的字数:28374490 完美字数:7621334 错误字数:20753156

###Lucene插件:

1、构造一个word分析器ChineseWordAnalyzer
Analyzer analyzer = new ChineseWordAnalyzer();

2、利用word分析器切分文本
TokenStream tokenStream = analyzer.tokenStream("text", "杨尚川是APDPlat应用级产品开发平台的作者");
while(tokenStream.incrementToken()){
	CharTermAttribute charTermAttribute = tokenStream.getAttribute(CharTermAttribute.class);
	OffsetAttribute offsetAttribute = tokenStream.getAttribute(OffsetAttribute.class);
	System.out.println(charTermAttribute.toString()+" "+offsetAttribute.startOffset());
}

3、利用word分析器建立Lucene索引
Directory directory = new RAMDirectory();
IndexWriterConfig config = new IndexWriterConfig(Version.LUCENE_47, analyzer);
IndexWriter indexWriter = new IndexWriter(directory, config);

4、利用word分析器查询Lucene索引
QueryParser queryParser = new QueryParser(Version.LUCENE_47, "text", analyzer);
Query query = queryParser.parse("text:杨尚川");
TopDocs docs = indexSearcher.search(query, Integer.MAX_VALUE);

###Solr插件:

1、下载word-1.1.jar
下载地址:http://search.maven.org/remotecontent?filepath=org/apdplat/word/1.1/word-1.1.jar

2、创建目录solr-4.7.1/example/solr/lib,将word-1.1.jar复制到lib目录

3、配置schema指定分词器
将solr-4.7.1/example/solr/collection1/conf/schema.xml文件中所有的
<tokenizer class="solr.WhitespaceTokenizerFactory"/>和
<tokenizer class="solr.StandardTokenizerFactory"/>全部替换为
<tokenizer class="org.apdplat.word.solr.ChineseWordTokenizerFactory"/>
并移除所有的filter标签

4、如果需要使用特定的分词算法:
<tokenizer class="org.apdplat.word.solr.ChineseWordTokenizerFactory" segAlgorithm="ReverseMinimumMatching"/>
segAlgorithm可选值有:	 
正向最大匹配算法:MaximumMatching
逆向最大匹配算法:ReverseMaximumMatching
正向最小匹配算法:MinimumMatching
逆向最小匹配算法:ReverseMinimumMatching
双向最大匹配算法:BidirectionalMaximumMatching
双向最小匹配算法:BidirectionalMinimumMatching
双向最大最小匹配算法:BidirectionalMaximumMinimumMatching
全切分算法:FullSegmentation
如不指定,默认使用双向最大匹配算法:BidirectionalMaximumMatching

5、如果需要指定特定的配置文件:
<tokenizer class="org.apdplat.word.solr.ChineseWordTokenizerFactory" segAlgorithm="ReverseMinimumMatching"
		conf="C:/solr-4.7.0/example/solr/nutch/conf/word.local.conf"/>
word.local.conf文件中可配置的内容见 word-1.1.jar 中的word.conf文件
如不指定,使用默认配置文件,位于 word-1.1.jar 中的word.conf文件

###ElasticSearch插件:

1、打开命令行并切换到elasticsearch的bin目录
	cd elasticsearch-1.2.1/bin

2、运行plugin脚本安装word分词插件:
	plugin -u http://apdplat.org/word/archive/v1.1.zip -i word

3、修改文件elasticsearch-1.2.1/config/elasticsearch.yml,新增如下配置:	
	index.analysis.analyzer.default.type : "word"
	index.analysis.tokenizer.default.type : "word"

4、启动ElasticSearch测试效果,在Chrome浏览器中访问:	
	http://localhost:9200/_analyze?analyzer=word&text=杨尚川是APDPlat应用级产品开发平台的作者
	
5、自定义配置
	修改配置文件elasticsearch-1.2.1/plugins/word/word.local.conf
	
6、指定分词算法
	修改文件elasticsearch-1.2.1/config/elasticsearch.yml,新增如下配置:
	index.analysis.analyzer.default.segAlgorithm : "ReverseMinimumMatching"
	index.analysis.tokenizer.default.segAlgorithm : "ReverseMinimumMatching"
	
	这里segAlgorithm可指定的值有:
	正向最大匹配算法:MaximumMatching
	逆向最大匹配算法:ReverseMaximumMatching
	正向最小匹配算法:MinimumMatching
	逆向最小匹配算法:ReverseMinimumMatching
	双向最大匹配算法:BidirectionalMaximumMatching
	双向最小匹配算法:BidirectionalMinimumMatching
	双向最大最小匹配算法:BidirectionalMaximumMinimumMatching
	全切分算法:FullSegmentation
	如不指定,默认使用双向最大匹配算法:BidirectionalMaximumMatching

###词向量:

从大规模语料中统计一个词的上下文相关词,并用这些上下文相关词组成的向量来表达这个词。
通过计算词向量的相似性,即可得到词的相似性。
相似性的假设是建立在如果两个词的上下文相关词越相似,那么这两个词就越相似这个前提下的。

通过运行项目根目录下的脚本demo-word-vector-corpus.bat来体验word项目自带语料库的效果

如果有自己的文本内容,可以使用脚本demo-word-vector-file.bat来对文本分词、建立词向量、计算相似性

###分词算法文章:

1、中文分词算法 之 基于词典的正向最大匹配算法

2、中文分词算法 之 基于词典的逆向最大匹配算法

3、中文分词算法 之 词典机制性能优化与测试

4、中文分词算法 之 基于词典的正向最小匹配算法

5、中文分词算法 之 基于词典的逆向最小匹配算法

6、Java开源项目cws_evaluation:中文分词器分词效果评估

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