IK是基于字典的一款轻量级的中文分词工具包，可以通过elasticsearch的插件机制集成；
一、集成步骤
1.在elasticsearch的安装目录下的plugin下新建ik目录；
2.在github下载对应版本的ik插件；
https://github.com/medcl/elasticsearch-analysis-ik/releases/tag/v6.8.12
3.解压插件文件，并重启elasticsearch，可以看到如下已经加载了ik插件；
[2022-01-11T15:22:54,341][INFO ][o.e.p.PluginsService ] [4EvvJl1] loaded plugin [analysis-ik]
二、体验IK的分析器
IK提供了ik_smart和ik_max_word两个分析器；
ik_max_word分析器会最大程度的对文本进行分词，分词的粒度还是比较细致的；
POST _analyze
{
"analyzer": "ik_max_word",
"text":"这次出差我们住的是闫团如家快捷酒店"
}
{
"tokens" : [
{
"token" : "这次",
"start_offset" : 0,
"end_offset" : 2,
"type" : "CN_WORD",
"position" : 0
},
{
"token" : "出差",
"start_offset" : 2,
"end_offset" : 4,
"type" : "CN_WORD",
"position" : 1
},
{
"token" : "我们",
"start_offset" : 4,
"end_offset" : 6,
"type" : "CN_WORD",
"position" : 2
},
{
"token" : "住",
"start_offset" : 6,
"end_offset" : 7,
"type" : "CN_CHAR",
"position" : 3
},
{
"token" : "的",
"start_offset" : 7,
"end_offset" : 8,
"type" : "CN_CHAR",
"position" : 4
},
{
"token" : "是",
"start_offset" : 8,
"end_offset" : 9,
"type" : "CN_CHAR",
"position" : 5
},
{
"token" : "闫",
"start_offset" : 9,
"end_offset" : 10,
"type" : "CN_CHAR",
"position" : 6
},
{
"token" : "团",
"start_offset" : 10,
"end_offset" : 11,
"type" : "CN_CHAR",
"position" : 7
},
{
"token" : "如家",
"start_offset" : 11,
"end_offset" : 13,
"type" : "CN_WORD",
"position" : 8
},
{
"token" : "快捷酒店",
"start_offset" : 13,
"end_offset" : 17,
"type" : "CN_WORD",
"position" : 9
}
]
}
ik_smart相对来说粒度会比较粗；
POST _analyze
{
"analyzer": "ik_smart",
"text":"这次出差我们住的是闫团如家快捷酒店"
}
{
"tokens" : [
{
"token" : "这次",
"start_offset" : 0,
"end_offset" : 2,
"type" : "CN_WORD",
"position" : 0
},
{
"token" : "出差",
"start_offset" : 2,
"end_offset" : 4,
"type" : "CN_WORD",
"position" : 1
},
{
"token" : "我们",
"start_offset" : 4,
"end_offset" : 6,
"type" : "CN_WORD",
"position" : 2
},
{
"token" : "住",
"start_offset" : 6,
"end_offset" : 7,
"type" : "CN_CHAR",
"position" : 3
},
{
"token" : "的",
"start_offset" : 7,
"end_offset" : 8,
"type" : "CN_CHAR",
"position" : 4
},
{
"token" : "是",
"start_offset" : 8,
"end_offset" : 9,
"type" : "CN_CHAR",
"position" : 5
},
{
"token" : "闫",
"start_offset" : 9,
"end_offset" : 10,
"type" : "CN_CHAR",
"position" : 6
},
{
"token" : "团",
"start_offset" : 10,
"end_offset" : 11,
"type" : "CN_CHAR",
"position" : 7
},
{
"token" : "如家",
"start_offset" : 11,
"end_offset" : 13,
"type" : "CN_WORD",
"position" : 8
},
{
"token" : "快捷酒店",
"start_offset" : 13,
"end_offset" : 17,
"type" : "CN_WORD",
"position" : 9
}
]
}
三、扩展ik字典
由于 闫团 是一个比较小的地方，ik的字典中并不包含导致分成两个单个的字符；我们可以将它添加到ik的字典中；
在ik的安装目录下config中新增my.dic文件，并将 闫团 放到文件中；完成之后修改IKAnalyzer.cfg.xml文件，添加新增的字典文件；
<properties>
<comment>IK Analyzer 扩展配置</comment>
<!--用户可以在这里配置自己的扩展字典 -->
<entry key="ext_dict">my.dic</entry>
<!--用户可以在这里配置自己的扩展停止词字典-->
<entry key="ext_stopwords"></entry>
<!--用户可以在这里配置远程扩展字典 -->
<!-- <entry key="remote_ext_dict">words_location</entry> -->
<!--用户可以在这里配置远程扩展停止词字典-->
<!-- <entry key="remote_ext_stopwords">words_location</entry> -->
</properties>
重启elasticsearch并重新执行查看已经将地名作为一个分词了；
POST _analyze
{
"analyzer": "ik_smart",
"text":"这次出差我们住的是闫团如家快捷酒店"
}
{
"tokens" : [
{
"token" : "这次",
"start_offset" : 0,
"end_offset" : 2,
"type" : "CN_WORD",
"position" : 0
},
{
"token" : "出差",
"start_offset" : 2,
"end_offset" : 4,
"type" : "CN_WORD",
"position" : 1
},
{
"token" : "我们",
"start_offset" : 4,
"end_offset" : 6,
"type" : "CN_WORD",
"position" : 2
},
{
"token" : "住",
"start_offset" : 6,
"end_offset" : 7,
"type" : "CN_CHAR",
"position" : 3
},
{
"token" : "的",
"start_offset" : 7,
"end_offset" : 8,
"type" : "CN_CHAR",
"position" : 4
},
{
"token" : "是",
"start_offset" : 8,
"end_offset" : 9,
"type" : "CN_CHAR",
"position" : 5
},
{
"token" : "闫团",
"start_offset" : 9,
"end_offset" : 11,
"type" : "CN_WORD",
"position" : 6
},
{
"token" : "如家",
"start_offset" : 11,
"end_offset" : 13,
"type" : "CN_WORD",
"position" : 7
},
{
"token" : "快捷酒店",
"start_offset" : 13,
"end_offset" : 17,
"type" : "CN_WORD",
"position" : 8
}
]
}
四、体验HanLP分析器及自定义字典
HanLP是由一系列模型与算法组成的Java工具包，它从中文分词开始，覆盖词性标注、命名实体识别、句法分析、文本分类等常用的NLP任务，提供了丰富的API，被广泛用于Lucene、Solr和ES等搜索平台。就分词算法来说，它支持最短路分词、N-最短路分词和CRF分词等分词算法。
从以下地址下载hanLP插件包
https://github.com/KennFalcon/elasticsearch-analysis-hanlp/releases/download/v7.9.2/elasticsearch-analysis-hanlp-7.9.2.zip
安装hanLP插件包
bin\elasticsearch-plugin install file:///c:/elasticsearch-analysis-hanlp-7.9.2.zip
-> Installing file:///c:/elasticsearch-analysis-hanlp-7.9.2.zip
-> Downloading file:///c:/elasticsearch-analysis-hanlp-7.9.2.zip
[=================================================] 100%??
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@ WARNING: plugin requires additional permissions @
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
* java.io.FilePermission plugins/analysis-hanlp/data/-#plus read,write,delete
* java.io.FilePermission plugins/analysis-hanlp/hanlp.cache#plus read,write,delete
* java.lang.RuntimePermission getClassLoader
* java.lang.RuntimePermission setContextClassLoader
* java.net.SocketPermission * connect,resolve
* java.util.PropertyPermission * read,write
See http://docs.oracle.com/javase/8/docs/technotes/guides/security/permissions.html
for descriptions of what these permissions allow and the associated risks.
Continue with installation? [y/N]y
-> Installed analysis-hanlp
使用hanlp_standard分析器对文本进行分析
POST _analyze
{
"analyzer": "hanlp_standard",
"text":"这次出差我们住的是闫团如家快捷酒店"
}
{
"tokens" : [
{
"token" : "这次",
"start_offset" : 0,
"end_offset" : 2,
"type" : "r",
"position" : 0
},
{
"token" : "出差",
"start_offset" : 2,
"end_offset" : 4,
"type" : "vi",
"position" : 1
},
{
"token" : "我们",
"start_offset" : 4,
"end_offset" : 6,
"type" : "rr",
"position" : 2
},
{
"token" : "住",
"start_offset" : 6,
"end_offset" : 7,
"type" : "vi",
"position" : 3
},
{
"token" : "的",
"start_offset" : 7,
"end_offset" : 8,
"type" : "ude1",
"position" : 4
},
{
"token" : "是",
"start_offset" : 8,
"end_offset" : 9,
"type" : "vshi",
"position" : 5
},
{
"token" : "闫团",
"start_offset" : 9,
"end_offset" : 11,
"type" : "nr",
"position" : 6
},
{
"token" : "如家",
"start_offset" : 11,
"end_offset" : 13,
"type" : "r",
"position" : 7
},
{
"token" : "快捷酒店",
"start_offset" : 13,
"end_offset" : 17,
"type" : "ntch",
"position" : 8
}
]
}
我们可以看到hanLP自动将 闫团 分成一个词了；
执行如下测试，可以看到hanLP没有将 小地方作为一个分词；
POST _analyze
{
"analyzer": "hanlp_standard",
"text":"闫团是一个小地方"
}
{
"tokens" : [
{
"token" : "闫团",
"start_offset" : 0,
"end_offset" : 2,
"type" : "nr",
"position" : 0
},
{
"token" : "是",
"start_offset" : 2,
"end_offset" : 3,
"type" : "vshi",
"position" : 1
},
{
"token" : "一个",
"start_offset" : 3,
"end_offset" : 5,
"type" : "mq",
"position" : 2
},
{
"token" : "小",
"start_offset" : 5,
"end_offset" : 6,
"type" : "a",
"position" : 3
},
{
"token" : "地方",
"start_offset" : 6,
"end_offset" : 8,
"type" : "n",
"position" : 4
}
]
}
为了自定义分词，我们在${ES_HOME}/plugins/analysis-hanlp/data/dictionary/custom下新建my.dic,并添加 小地方；
然后从插件安装包拷贝hanlp.properties文件放到如下位置${ES_HOME}/config/analysis-hanlp/hanlp.properties，并修改CustomDictionaryPath；
CustomDictionaryPath=data/dictionary/custom/CustomDictionary.txt; ModernChineseSupplementaryWord.txt; ChinesePlaceName.txt ns; PersonalName.txt; OrganizationName.txt; ShanghaiPlaceName.txt ns;data/dictionary/person/nrf.txt nrf;data/dictionary/custom/my.dic;
从起elasticsearch并执行测试
POST _analyze
{
"analyzer": "hanlp",
"text":"闫团是一个小地方"
}
{
"tokens" : [
{
"token" : "闫团",
"start_offset" : 0,
"end_offset" : 2,
"type" : "nr",
"position" : 0
},
{
"token" : "是",
"start_offset" : 2,
"end_offset" : 3,
"type" : "vshi",
"position" : 1
},
{
"token" : "一个",
"start_offset" : 3,
"end_offset" : 5,
"type" : "mq",
"position" : 2
},
{
"token" : "小地方",
"start_offset" : 5,
"end_offset" : 8,
"type" : "n",
"position" : 3
}
]
}