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背 景
之所以选择用ES,自然是看重了她的倒排所以,倒排索引又必然关联到分词的逻辑,此处就以中文分词为例以下说到的分词指的就是中文分词,ES本身默认的分词是将每个汉字逐个分开,具体如下,自然是很弱的,无法满足业务需求,那么就需要把那些优秀的分词器融入到ES中来,业界比较好的中文分词器排名如下,hanlp
> ansj
>结巴
>ik
>smart chinese analysis
;
博主这里就选两种比较常用的讲解hanlp
和ik
,hanlp
在业界名声最响,ik
是官方推荐和ES版本同步更新的使用最多的分词器,并且举例比较下他们的功能;
断句对比效果
默认的分词器效果;
GET /_analyze
{"text": "林俊杰在上海市开演唱会啦"
}# 结果
{"tokens" : [{"token" : "林","start_offset" : 0,"end_offset" : 1,"type" : "<IDEOGRAPHIC>","position" : 0},{"token" : "俊","start_offset" : 1,"end_offset" : 2,"type" : "<IDEOGRAPHIC>","position" : 1},{"token" : "杰","start_offset" : 2,"end_offset" : 3,"type" : "<IDEOGRAPHIC>","position" : 2},{"token" : "在","start_offset" : 3,"end_offset" : 4,"type" : "<IDEOGRAPHIC>","position" : 3},{"token" : "上","start_offset" : 4,"end_offset" : 5,"type" : "<IDEOGRAPHIC>","position" : 4},{"token" : "海","start_offset" : 5,"end_offset" : 6,"type" : "<IDEOGRAPHIC>","position" : 5},{"token" : "市","start_offset" : 6,"end_offset" : 7,"type" : "<IDEOGRAPHIC>","position" : 6},{"token" : "开","start_offset" : 7,"end_offset" : 8,"type" : "<IDEOGRAPHIC>","position" : 7},{"token" : "演","start_offset" : 8,"end_offset" : 9,"type" : "<IDEOGRAPHIC>","position" : 8},{"token" : "唱","start_offset" : 9,"end_offset" : 10,"type" : "<IDEOGRAPHIC>","position" : 9},{"token" : "会","start_offset" : 10,"end_offset" : 11,"type" : "<IDEOGRAPHIC>","position" : 10},{"token" : "啦","start_offset" : 11,"end_offset" : 12,"type" : "<IDEOGRAPHIC>","position" : 11}]
}
ik分词器效果,这里以ik_smart为例;
GET /_analyze
{"text": "林俊杰在上海市开演唱会啦","analyzer": "ik_smart"
}# 结果
{
"tokens" : [{"token" : "林俊杰","start_offset" : 0,"end_offset" : 3,"type" : "CN_WORD","position" : 0},{"token" : "在上","start_offset" : 3,"end_offset" : 5,"type" : "CN_WORD","position" : 1},{"token" : "海市","start_offset" : 5,"end_offset" : 7,"type" : "CN_WORD","position" : 2},{"token" : "开","start_offset" : 7,"end_offset" : 8,"type" : "CN_CHAR","position" : 3},{"token" : "演唱会","start_offset" : 8,"end_offset" : 11,"type" : "CN_WORD","position" : 4},{"token" : "啦","start_offset" : 11,"end_offset" : 12,"type" : "CN_CHAR","position" : 5}
]
}
hanlp分词器效果,这里以hanlp默认分词器为例;
GET /_analyze
{"text": "林俊杰在上海市开演唱会啦","analyzer": "hanlp"
}# 结果如下
{
"tokens" : [{"token" : "林俊杰","start_offset" : 0,"end_offset" : 3,"type" : "nr","position" : 0},{"token" : "在","start_offset" : 3,"end_offset" : 4,"type" : "p","position" : 1},{"token" : "上海市","start_offset" : 4,"end_offset" : 7,"type" : "ns","position" : 2},{"token" : "开","start_offset" : 7,"end_offset" : 8,"type" : "v","position" : 3},{"token" : "演唱会","start_offset" : 8,"end_offset" : 11,"type" : "n","position" : 4},{"token" : "啦","start_offset" : 11,"end_offset" : 12,"type" : "y","position" : 5}
]
}
断句层面,hanlp还是要强于ik的;
ik安装
- 官网找到和ES版本的
elasticsearch-analysis-ik-7.7.1.zip
,下载安装zip包,如图1;
官网地址
- 将下载的
elasticsearch-analysis-ik-7.7.1.zip
上传到elasticsearch 的安装目录下的plugins下,如我的是/usr/local/tools/elasticsearch/elasticsearch-7.7.1/plugins
,当然,你集群要是网速不错,也可以在家此文件夹下直接下载,省去上传的工作;
cd /usr/local/tools/elasticsearch/elasticsearch-7.7.1/plugins
#直接下载指令
wget https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.7.1/elasticsearch-analysis-ik-7.7.1.zip
- 解压
/usr/local/tools/elasticsearch/elasticsearch-7.7.1/plugins``下的
elasticsearch-analysis-ik-7.7.1.zip`包,指令如下;
#因为是zip,如果报错unzip不是内部指令。说明没安装unzip需要先安装,如果已安装,直接跳过这里
yum install zip
yum install unzip#新建id文件夹
mkdir ik#将zip包移入刚刚新建ik文件夹呢
mv ./elasticsearch-analysis-ik-7.7.1.zip ik/#进入ik文件夹
cd ik#解压
unzip elasticsearch-analysis-ik-7.7.1.zip#解压后确保里面的问价如下
total 5828
-rwxr-xr-x 1 hadoop supergroup 263965 Aug 5 18:57 commons-codec-1.9.jar
-rwxr-xr-x 1 hadoop supergroup 61829 Aug 5 18:57 commons-logging-1.2.jar
drwxrwxrwx 2 hadoop supergroup 299 Aug 5 18:57 config
-rwxr-xr-x 1 hadoop supergroup 54599 Aug 5 18:57 elasticsearch-analysis-ik-7.7.1.jar
-rwxr-xr-x 1 hadoop supergroup 4504441 Aug 5 18:57 elasticsearch-analysis-ik-7.7.1.zip
-rwxr-xr-x 1 hadoop supergroup 736658 Aug 5 18:57 httpclient-4.5.2.jar
-rwxr-xr-x 1 hadoop supergroup 326724 Aug 5 18:57 httpcore-4.4.4.jar
-rwxr-xr-x 1 hadoop supergroup 1805 Aug 5 18:57 plugin-descriptor.properties
-rwxr-xr-x 1 hadoop supergroup 125 Aug 5 18:57 plugin-security.policy#赋权
chmod -R 777 ./*#切换到es的安装目录
cd /usr/local/tools/elasticsearch/elasticsearch-7.7.1/#查看是否安装完成
bin/elasticsearch-plugin list
#返回结果
future versions of Elasticsearch will require Java 11; your Java version from [/usr/local/tools/java/jdk1.8.0_211/jre] does not meet this requirement
ik
- 重启es,让分词器生效,操作shell如下;
# 利用jps查看elasticsearch的守护进程
jps
#结果
2497 Kafka
2609 QuorumPeerMain
23906 Elasticsearch
32282 NodeManager
2428 Jps
7341 Worker
2126 CoarseGrainedExecutorBackend#杀死elasticsearch的守护进程
kill -9 23906 #重启启动es
bin/elasticsearch -d
- 确保整个es集群上的每台机器都操作了以上步骤后,就可以在kibana上测试了,kibana RESTFul风格的测试语句如下;
GET /_analyze
{"text": "林俊杰在上海市开演唱会啦","analyzer": "ik_smart"
}# 结果
{"tokens" : [{"token" : "林俊杰","start_offset" : 0,"end_offset" : 3,"type" : "CN_WORD","position" : 0},{"token" : "在上","start_offset" : 3,"end_offset" : 5,"type" : "CN_WORD","position" : 1},{"token" : "海市","start_offset" : 5,"end_offset" : 7,"type" : "CN_WORD","position" : 2},{"token" : "开","start_offset" : 7,"end_offset" : 8,"type" : "CN_CHAR","position" : 3},{"token" : "演唱会","start_offset" : 8,"end_offset" : 11,"type" : "CN_WORD","position" : 4},{"token" : "啦","start_offset" : 11,"end_offset" : 12,"type" : "CN_CHAR","position" : 5}]
}
更多的ik分词器结合es的使用,请查考ik的官网readme教程:传送门
hanlp安装
hanlp
在es的使用有很多人在做,版本相对比较乱,博主也是找了好几个版本,终于选了一个博主用的来的,hanlp
的安装稍微会比ik
繁琐一丢丢,所以大家也稍微耐心点;
hanlp并没有做到和ES版本的同步更新,所以遇到较新的版本,则需要自己编译源码打包!比如我们的ElasticSearch7.7.1就是目前(20201225)没有release版本!而且hanlp分词器不能直接找hanlp包,用不了,而是要找和elasticsearch兼容的elasticsearch-analysis-hanlp
- 进入其中一个elasticsearch-analysis-hanlp派系的官网,如图2:传送门
-
利用git,在文件夹内
git clone https://github.com/AnyListen/elasticsearch-analysis-hanlp.git
,再利用java的开发工具IDEA或者eclipse打开项目,打开 pom.xml 文件,修改 <elasticsearch.version>7.0.0</elasticsearch.version> 为需要的 ES 版本; -
这个git项目的老哥太大意了,留了个bug,如下图3d的文件内缺少两个参数
name
,你把它补全加上,不然编译报错,然后使用 mvn package 生产打包文件,最终文件在 target/release 文件夹下,打包完成后,使用离线方式安装即可。
.
- 在es的插件目录下
/usr/local/tools/elasticsearch/elasticsearch-7.7.1/plugins
新建`hanlp1文件夹,开始离线安装,代码如下;
#进入es插件目录
cd /usr/local/tools/elasticsearch/elasticsearch-7.7.1/plugins#新建hanlp文件夹并进入
mkdir hanlp
chmod 755 hanlp
cd hanlp#将之前重新编译打包好的 target/release下的elasticsearch-analysis-hanlp-7.7.1.zip上传到新建的hanlp目录下解压
unzip elasticsearch-analysis-hanlp-7.7.1.zip#解压后目录如下
-rwxr-xr-x 1 hadoop supergroup 33498 Dec 24 15:24 elasticsearch-analysis-hanlp-7.7.1.jar
-rw-r--r-- 1 hadoop supergroup 7747506 Dec 24 15:24 elasticsearch-analysis-hanlp-7.7.1.zip
-rwxr-xr-x 1 hadoop supergroup 7971652 Dec 24 15:24 hanlp-portable-1.7.3.jar
-rwxr-xr-x 1 hadoop supergroup 2493 Dec 24 15:24 hanlp.properties
-rwxr-xr-x 1 hadoop supergroup 1117 Dec 24 15:24 plugin-descriptor.properties
-rwxr-xr-x 1 hadoop supergroup 88 Dec 24 15:24 plugin.properties
-rwxr-xr-x 1 hadoop supergroup 414 Dec 24 15:24 plugin-security.policy#赋权
chmod -R 755 ./*#利用vi修改hanlp.properties里面的root=的值,为es的hanlp插件安装目录,如下
root=/usr/local/tools/elasticsearch/elasticsearch-7.7.1/plugins/hanlp/
#wq!保存hanlp.properties的内容#汇到es的安装目录查看hanlp分词器是否成功
cd /usr/local/tools/elasticsearch/elasticsearch-7.7.1/
bin/elasticsearch-plugin list
#返回结果
future versions of Elasticsearch will require Java 11; your Java version from [/usr/local/tools/java/jdk1.8.0_211/jre] does not meet this requirement
hanlp
ik
- 重启es,让分词器生效,操作shell如下;
# 利用jps查看elasticsearch的守护进程
jps
#结果
2497 Kafka
2609 QuorumPeerMain
24812 Elasticsearch
32282 NodeManager
2428 Jps
7341 Worker
2126 CoarseGrainedExecutorBackend#杀死elasticsearch的守护进程
kill -9 24812 #重启启动es
bin/elasticsearch -d
- 确保整个es集群上的每台机器都操作了以上步骤后,就可以在kibana上测试了,kibana RESTFul风格的测试语句如下;
GET /_analyze
{"text": "林俊杰在上海市开演唱会啦","analyzer": "hanlp"
}# 结果如下
{
"tokens" : [{"token" : "林俊杰","start_offset" : 0,"end_offset" : 3,"type" : "nr","position" : 0},{"token" : "在","start_offset" : 3,"end_offset" : 4,"type" : "p","position" : 1},{"token" : "上海市","start_offset" : 4,"end_offset" : 7,"type" : "ns","position" : 2},{"token" : "开","start_offset" : 7,"end_offset" : 8,"type" : "v","position" : 3},{"token" : "演唱会","start_offset" : 8,"end_offset" : 11,"type" : "n","position" : 4},{"token" : "啦","start_offset" : 11,"end_offset" : 12,"type" : "y","position" : 5}
]
}
  <font face='times new roman' color=blue>**更多的hanlp分词器结合es的使用,请查考hanlp某一派系的的官网readme教程:[传送门](https://github.com/anylisten/elasticsearch-analysis-hanlp)**
## ==<font color='blue' face='楷体'>专有名词对比效果</font>==  <font face='times new roman'>默认的分词器效果;```json
GET /_analyze
{"text": "中国移动"
}#结果
{"tokens" : [{"token" : "中","start_offset" : 0,"end_offset" : 1,"type" : "<IDEOGRAPHIC>","position" : 0},{"token" : "国","start_offset" : 1,"end_offset" : 2,"type" : "<IDEOGRAPHIC>","position" : 1},{"token" : "移","start_offset" : 2,"end_offset" : 3,"type" : "<IDEOGRAPHIC>","position" : 2},{"token" : "动","start_offset" : 3,"end_offset" : 4,"type" : "<IDEOGRAPHIC>","position" : 3}]
}
ik分词器效果,这里以ik_smart为例;
GET /_analyze
{"text": "中国移动","analyzer": "ik_smart"
}#结果
{
"tokens" : [{"token" : "中国移动","start_offset" : 0,"end_offset" : 4,"type" : "CN_WORD","position" : 0}
]
}
hanlp分词器效果,这里以hanlp默认分词器为例;
GET /_analyze
{"text": "中国移动","analyzer": "hanlp"
}#结果如下
{
"tokens" : [{"token" : "中国","start_offset" : 0,"end_offset" : 2,"type" : "ns","position" : 0},{"token" : "移动","start_offset" : 2,"end_offset" : 4,"type" : "vn","position" : 1}
]
}
专有名词上,hanlp和ik的各有特殊,读者也可自己多测试几轮,而且ik和hanlp自带网页版的在线分词器,只需要百度搜索ik活hanlp在线分词即可使用;
维护自己的词典
当然不论采用哪种分词器,都不能一劳永逸解决所有的分词匹配需求,特别是针对某些特有的分词需求,如当搜索自家公司或者自家公司产品时,期望他得分靠前,这个时候就需要维护自己的词典,ik和hanlp都支持维护自己的词典,即当你规定某个词为一体时,该词不会再做细分;具体操作可以查看各自官网的readme文件有说明。