Elasticsearch7.x使用(四) IK分詞插件

未安裝分詞插件之前只能使用默認的分詞規則：

1)普通分詞
GET _analyze
{
  "text": ["他是一個前端開發工程師"],
  "analyzer": "standard"
}
或者：
2)全文分詞
GET _analyze
{
  "text": ["他是一個前端開發工程師"],
  "analyzer": "keyword"
}

分詞結果：

{
  "tokens" : [
    {
      "token" : "他",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "是",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "一",
      "start_offset" : 2,
      "end_offset" : 3,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "個",
      "start_offset" : 3,
      "end_offset" : 4,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    },
    {
      "token" : "前",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<IDEOGRAPHIC>",
      "position" : 4
    },
    {
      "token" : "端",
      "start_offset" : 5,
      "end_offset" : 6,
      "type" : "<IDEOGRAPHIC>",
      "position" : 5
    },
    {
      "token" : "開",
      "start_offset" : 6,
      "end_offset" : 7,
      "type" : "<IDEOGRAPHIC>",
      "position" : 6
    },
    {
      "token" : "發",
      "start_offset" : 7,
      "end_offset" : 8,
      "type" : "<IDEOGRAPHIC>",
      "position" : 7
    },
    {
      "token" : "工",
      "start_offset" : 8,
      "end_offset" : 9,
      "type" : "<IDEOGRAPHIC>",
      "position" : 8
    },
    {
      "token" : "程",
      "start_offset" : 9,
      "end_offset" : 10,
      "type" : "<IDEOGRAPHIC>",
      "position" : 9
    },
    {
      "token" : "師",
      "start_offset" : 10,
      "end_offset" : 11,
      "type" : "<IDEOGRAPHIC>",
      "position" : 10
    }
  ]
}

 

使用analysis-ik分詞插件

1、安裝

[elasticsearch@txvm2019 bin]./elasticsearch-plugin install https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.2.0/elasticsearch-analysis-ik-7.2.0.zip

安裝完成後可以使用list查看是否成功，

[elasticsearch@txvm2019 bin]$ ./elasticsearch-plugin list
analysis-icu
analysis-ik

2、測試：

GET _analyze
{
  "text": ["他是一個前端開發工程師"],
  "analyzer": "ik_max_word"
}

分詞結果：

{
  "tokens" : [
    {
      "token" : "他",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "CN_CHAR",
      "position" : 0
    },
    {
      "token" : "是",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "CN_CHAR",
      "position" : 1
    },
    {
      "token" : "一個",
      "start_offset" : 2,
      "end_offset" : 4,
      "type" : "CN_WORD",
      "position" : 2
    },
    {
      "token" : "一",
      "start_offset" : 2,
      "end_offset" : 3,
      "type" : "TYPE_CNUM",
      "position" : 3
    },
    {
      "token" : "個",
      "start_offset" : 3,
      "end_offset" : 4,
      "type" : "COUNT",
      "position" : 4
    },
    {
      "token" : "前端",
      "start_offset" : 4,
      "end_offset" : 6,
      "type" : "CN_WORD",
      "position" : 5
    },
    {
      "token" : "開發",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "CN_WORD",
      "position" : 6
    },
    {
      "token" : "工程師",
      "start_offset" : 8,
      "end_offset" : 11,
      "type" : "CN_WORD",
      "position" : 7
    },
    {
      "token" : "工程",
      "start_offset" : 8,
      "end_offset" : 10,
      "type" : "CN_WORD",
      "position" : 8
    },
    {
      "token" : "師",
      "start_offset" : 10,
      "end_offset" : 11,
      "type" : "CN_CHAR",
      "position" : 9
    }
  ]
}

3、說明：

IK分詞主要有以下兩種類型：
ik_max_word：將文本按最細粒度的組合來拆分；
ik_smart:：最粗粒度的拆分；
如果在分詞的時候不添加分詞類別，Elasticsearch對於漢字默認使用standard只是將漢字拆分成一個個的漢字。