elasticsearch之集成中文分词器

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
    }
  ]
}