Standard Analyzer

• 默认的analyzer，适合大多数语言。
• 根据Unicode Text Segmentation算法的定义，将文本切分成词元。
• 删除多数标点符号、将词元小写，支持删除停止词。

standard analyzer由以下构成：

• Standard Tokenizer
  • Standard Tokenizer
• Token Filters
  • Standard Token Filter
  • Lower Case Token Filter
  • Stop Token Filter（默认禁用）

Standard Analyzer 示例

POST _analyze
{
  "analyzer": "standard",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

产生[ the, 2, quick, brown, foxes, jumped, over, the, lazy, dog's, bone ]。

Standard Analyzer 配置

示例

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_english_analyzer": {
          "type": "standard",
          "max_token_length": 5,
          "stopwords": "_english_"
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_english_analyzer", 
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

产生[ 2, quick, brown, foxes, jumpe, d, over, lazy, dog's, bone ]。

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_array_analyzer": {
          "type": "standard",
          "stopwords": ["the","2","quick","brown","foxes","jumped","over","dog's","bone"]
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_array_analyzer", 
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

产生[lazy]。

Simple Analyzer

• simple analyzer遇到非字母就会切分，并且会小写。

simple analyzer由以下构成：

• Tokenizer
  • Lower Case Tokenizer

Simple Analyzer 示例

POST _analyze
{
  "analyzer": "simple",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

产生[ the, quick, brown, foxes, jumped, over, the, lazy, dog, s, bone ]。

Whitespace Analyzer

• 遇到空格符就切分

whitespace analyzer由以下构成：

• Tokenizer
  • Whitespace Tokenizer

Whitespace Analyzer 示例

POST _analyze
{
  "analyzer": "whitespace"
  , "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

产生[ The, 2, QUICK, Brown-Foxes, jumped, over, the, lazy, dog's, bone. ]。

Stop Analyzer

• 类似simple analyzer，但是支持删除停止词。默认使用_english_停止词。

stop analyzer由以下构成：

• Tokenizer
  • Lower Case Tokenizer
• Token filters
  • Stop Token Filter

Stop Analyzer 示例

POST _analyze
{
  "analyzer": "stop",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

产生[ quick, brown, foxes, jumped, over, lazy, dog, s, bone ]

Stop Analyzer 配置

示例

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_stop_analyzer":{
          "type": "stop",
          "stopwords":  ["the","2","quick","brown","foxes","jumped","over","dog","s","bone"]
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_stop_analyzer", 
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

产生[ lazy ]

Keyword Analyzer

• 不切分作为一整个词元输出。

keyword analyzer由以下构成：

• Tokenizer
  • Keyword Tokenizer

Keyword Analyzer 示例

POST _analyze
{
  "analyzer": "keyword", 
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

产生[ The 2 QUICK Brown-Foxes jumped over the lazy dog's bone. ]。

Pattern Analyzer

• 按照正则表示式去切分，默认为\W+

pattern analyzer由以下构成：

• Tokenizer
  • Pattern Tokenizer
• Token Filters
  • Lower Case Token Filter
  • Stop Token Filter (默认禁用)

Pattern Analyzer 示例

POST _analyze
{
  "analyzer": "pattern",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

产生[ the, 2, quick, brown, foxes, jumped, over, the, lazy, dog, s, bone ]

Pattern Analyzer 配置

示例

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_pattern_analyzer": {
          "type": "pattern",
          "pattern": "\\W|_",
          "lowercase": true
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_pattern_analyzer", 
  "text": "John_Smith@foo-bar.com"
}

产生[ john, smith, foo, bar, com ]。

Fingerprint Analyzer

• 小写，规范化删掉扩展符，排序，去重。
• 也可以配置停止符。
  fingerprint tokenizer 由以下构成：
• Tokenizer
  • Standard Tokenizer
• Token Filters（依次如下）
  • Lower Case Token Filter
  • ASCII Folding Token Filter
  • Stop Token Filter (默认禁用）
  • Fingerprint Token Filter

Fingerprint Analyzer 示例

POST _analyze
{
  "analyzer": "fingerprint",
  "text": "Yes yes, Gödel said this sentence is consistent and."
}

产生[ and consistent godel is said sentence this yes ]

Fingerprint Analyzer 配置

示例

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_fingerprint_analyzer":{
          "type": "fingerprint",
          "stopwords": "_english_"
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_fingerprint_analyzer",
  "text": "Yes yes, Gödel said this sentence is consistent and."
}

产生[ consistent godel said sentence yes ]

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_fingerprint_analyzer":{
          "type": "fingerprint",
          "stopwords": "_english_",
          "separator": "-"
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_fingerprint_analyzer",
  "text": "Yes yes, Gödel said this sentence is consistent and."
}

产生[ consistent-godel-said-sentence-yes ]

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_fingerprint_analyzer":{
          "type": "fingerprint",
          "max_output_size": 30
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_fingerprint_analyzer",
  "text": "Yes yes, Gödel said this sentence is consistent and."
}

什么都不产生，整条被丢弃，

补充说明

• Whitespace会遇到空格就拆分，而Standard则是提取出单词，例如：对于Brown-Foxes，Whitespace切分之后还是这样，而Standard切分后则是brown和foxes。
• Simple遇到非字母就切分，而Standard未必，例如：对于dog's，Simple会切分成dog和s，而Standard切分后则是dog's。
• 总之，Whitespace遇到空格就切分，Simple遇到非字母就切分，Standard切分单词（可以是所有格形式）。

自定义Analyzer

• 0或更多的character filter
• 一个tokenizer
• 0或更多的token filter

自定义Analyzer的配置

示例1：

• Character Filter
  • HTML Strip Character Filter
• Tokenizer
  • Standard Tokenizer
• Token Filters
  • Lowercase Token Filter
  • ASCII-Folding Token Filter

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_custom_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "char_filter":[
            "html_strip"
            ],
          "filter": [
            "lowercase",
            "asciifolding"
            ]
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_custom_analyzer",
  "text": "Is this <b>déjà vu</b>?"
}

产生[ is, this, deja, vu ]

示例2

• Character Filter
  • Mapping Character Filter：替换:)为_happy_以及 :( 为_sad_
• Tokenizer
  • Pattern Tokenizer
• Token Filters
  • Lowercase Token Filter
  • Stop Token Filter

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_custom_analyzer": {
          "type": "custom",
          "char_filter": [
              "emoticons"
            ],
          "tokenizer": "punctuation",
          "filter": [
            "lowercase",
            "english_stop"
            ]
        }
      },
      "tokenizer": {
        "punctuation": {
          "type": "pattern",
          "pattern": "[ .,!?]"
        }
      },
      "char_filter": {
        "emoticons": {
          "type": "mapping",
          "mappings": [
            ":) => _happy_",
            ":( => _sad_"
            ]
        }
      },
      "filter": {
        "english_stop":{
          "type": "stop",
          "stopwords": "_english_"
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_custom_analyzer",
  "text":     "I'm a :) person, and you?"
}

产生[ i'm, _happy_, person, you ]。