elasticsearch学习笔记(二)-elasticsearch分词器

elasticsear analyzer

什么是Analysis

顾名思义,文本分析就是把全文本转换成一系列单词(term/token)的过程,也叫分词。在 ES 中,Analysis 是通过分词器(Analyzer) 来实现的,可使用 ES 内置的分析器或者按需定制化分析器。

比如输入的文本是thinking in elasticsearch,通过分词器,可以分成三个单词,分别是thinking, in, elasticsearch

分词器的组成

  • Character Filters: 针对原始文本处理,比如去除 html 标签
  • Tokenizer: 按照规则切分为单词,比如按照空格切分
  • Token Filter: 将切分的单词进行加工,比如大写转小写,删除 stopwords,增加同义语

同时 Analyzer 三个部分也是有顺序的,从上到下依次经过 Character FiltersTokenizer 以及 Token Filters,这个顺序比较好理解,一个文本进来,需要先经过Character Filters进行文本数据处理,然后进入Tokenizer进行分词操作,最后对分词的结果进行过滤。

Elasticsearch内置分词器

Standard Analyzer

  • elasticsearch内置的标准分词器,如果没有指定,这就是默认选择的分词器。

  • 按词分配,支持多种语言

  • 小写处理,它删除大多数标点符号、小写术语,并支持指定需要删除的词语。

  • Standard Tokenizer

  • Lower Case Token Filter

  • Stop Token Filter (默认是禁止使用的)

    • 默认需要删除的词语有:

      a, an, and, are, as, at, be, but, by, for, if, in, into, is, it, no, not, of, on, or, such, that, the, their, then, there, these, they, this, to, was, will,with

Simple Analyzer

  • 小写处理
  • 如果文本中包含了非字母字符(数字,撇号,空格,连字符等),则会丢弃。
  • Lower Case Token Filter

Whitespace Analyzer

  • 按照空格切分
  • Whitespace Filter

Stop Analyzer

  • 相比Simple Analyzer多了Stop Filter,会把a, an, and, are, as, at, be, but, by, for, if, in, into, is, it, no, not, of, on, or, such, that, the, their, then, there, these, they, this, to, was, will,with等修饰词去除。

Keyword Analyzer

  • 不分词,直接将输入当一个term输出
  • Keyword Filter

Pattern Analyzer

  • 通过正则表达式进行分词。
  • 默认是\W+,非字符的符号进行隔离
  • Pattern Tokenizer
  • Lower Case Token Filter
  • Stop Token Filter(disable by default)

Language Analyzer

A set of analyzers aimed at analyzing specific language text. The following types are supported: arabic, armenian, basque, bengali, brazilian, bulgarian, catalan, cjk, czech, danish, dutch, english, estonian, finnish, french, galician, german, greek, hindi, hungarian, indonesian, irish, italian, latvian, lithuanian, norwegian, persian, portuguese, romanian, russian, sorani, spanish, swedish, turkish, thai.

Fingerprint Analyzer

  • 指纹分词器,转小写,删除扩展字符,排序,删除重复字符

Custom Analyzer

中文分词的难点

  • 中文句子,切分成一个个词(不是一个个子)
  • 英文中,单词与单词之间有空格作为分割,而中文的语句中,词与词没有什么可以自然分割的。
  • 一些中文语句,在不同的上下文中有不同的理解
    • 这个苹果,不大好吃/这个苹果,不大,好吃

中文分词器

ICU Analyzer
  • 需要安装plugin
    • Elasticsearch-plugin install analysis-icu
  • 提供了unicode的支持,更好的支持亚洲语言
  • Normalization Character Filter
  • ICU Tokenizer
  • Normalization Token Filter
  • Folding Token Filter
  • Collation Token Filter
  • Transform Token Filter
IK Analyzer

参考资料:medcl github,提供了很多custom analyzer

分词器API

Standard Analyzer

  • 默认配置

语句:

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

结果:

{
  "tokens" : [
    {
      "token" : "the",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "2",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<NUM>",
      "position" : 1
    },
    {
      "token" : "quick",
      "start_offset" : 6,
      "end_offset" : 11,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "brown",
      "start_offset" : 12,
      "end_offset" : 17,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "foxes",
      "start_offset" : 18,
      "end_offset" : 23,
      "type" : "<ALPHANUM>",
      "position" : 4
    },
    {
      "token" : "jumped",
      "start_offset" : 24,
      "end_offset" : 30,
      "type" : "<ALPHANUM>",
      "position" : 5
    },
    {
      "token" : "over",
      "start_offset" : 31,
      "end_offset" : 35,
      "type" : "<ALPHANUM>",
      "position" : 6
    },
    {
      "token" : "the",
      "start_offset" : 36,
      "end_offset" : 39,
      "type" : "<ALPHANUM>",
      "position" : 7
    },
    {
      "token" : "lazy",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "<ALPHANUM>",
      "position" : 8
    },
    {
      "token" : "dog's",
      "start_offset" : 45,
      "end_offset" : 50,
      "type" : "<ALPHANUM>",
      "position" : 9
    },
    {
      "token" : "bone",
      "start_offset" : 51,
      "end_offset" : 55,
      "type" : "<ALPHANUM>",
      "position" : 10
    }
  ]
}
  • 修改配置,启用stop token filter

语句:

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

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

结果:

{
  "tokens" : [
    {
      "token" : "2",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<NUM>",
      "position" : 1
    },
    {
      "token" : "quick",
      "start_offset" : 6,
      "end_offset" : 11,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "brown",
      "start_offset" : 12,
      "end_offset" : 17,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "foxes",
      "start_offset" : 18,
      "end_offset" : 23,
      "type" : "<ALPHANUM>",
      "position" : 4
    },
    {
      "token" : "jumpe",
      "start_offset" : 24,
      "end_offset" : 29,
      "type" : "<ALPHANUM>",
      "position" : 5
    },
    {
      "token" : "d",
      "start_offset" : 29,
      "end_offset" : 30,
      "type" : "<ALPHANUM>",
      "position" : 6
    },
    {
      "token" : "over",
      "start_offset" : 31,
      "end_offset" : 35,
      "type" : "<ALPHANUM>",
      "position" : 7
    },
    {
      "token" : "lazy",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "<ALPHANUM>",
      "position" : 9
    },
    {
      "token" : "dog's",
      "start_offset" : 45,
      "end_offset" : 50,
      "type" : "<ALPHANUM>",
      "position" : 10
    },
    {
      "token" : "bone",
      "start_offset" : 51,
      "end_offset" : 55,
      "type" : "<ALPHANUM>",
      "position" : 11
    }
  ]
}

Simple Analyzer

语句:

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

结果:

{
  "tokens" : [
    {
      "token" : "the",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "quick",
      "start_offset" : 6,
      "end_offset" : 11,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "brown",
      "start_offset" : 12,
      "end_offset" : 17,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "foxes",
      "start_offset" : 18,
      "end_offset" : 23,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "jumped",
      "start_offset" : 24,
      "end_offset" : 30,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "over",
      "start_offset" : 31,
      "end_offset" : 35,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "the",
      "start_offset" : 36,
      "end_offset" : 39,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "lazy",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "dog",
      "start_offset" : 45,
      "end_offset" : 48,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "s",
      "start_offset" : 49,
      "end_offset" : 50,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "bone",
      "start_offset" : 51,
      "end_offset" : 55,
      "type" : "word",
      "position" : 10
    }
  ]
}

Stop Analyzer

语句:

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

结果:

{
  "tokens" : [
    {
      "token" : "quick",
      "start_offset" : 6,
      "end_offset" : 11,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "brown",
      "start_offset" : 12,
      "end_offset" : 17,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "foxes",
      "start_offset" : 18,
      "end_offset" : 23,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "jumped",
      "start_offset" : 24,
      "end_offset" : 30,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "over",
      "start_offset" : 31,
      "end_offset" : 35,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "lazy",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "dog",
      "start_offset" : 45,
      "end_offset" : 48,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "s",
      "start_offset" : 49,
      "end_offset" : 50,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "bone",
      "start_offset" : 51,
      "end_offset" : 55,
      "type" : "word",
      "position" : 10
    }
  ]
}

自定义过滤条件:

PUT stop-demo
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_stop_analyzer": {
          "type": "stop",
          "stopwords": ["the", "over"]
        }
      }
    }
  }
}

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

结果:

{
  "tokens" : [
    {
      "token" : "quick",
      "start_offset" : 6,
      "end_offset" : 11,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "brown",
      "start_offset" : 12,
      "end_offset" : 17,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "foxes",
      "start_offset" : 18,
      "end_offset" : 23,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "jumped",
      "start_offset" : 24,
      "end_offset" : 30,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "lazy",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "dog",
      "start_offset" : 45,
      "end_offset" : 48,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "s",
      "start_offset" : 49,
      "end_offset" : 50,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "bone",
      "start_offset" : 51,
      "end_offset" : 55,
      "type" : "word",
      "position" : 10
    }
  ]
}

Keyword Analyzer

语句:

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

结果:

{
  "tokens" : [
    {
      "token" : "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone.",
      "start_offset" : 0,
      "end_offset" : 56,
      "type" : "word",
      "position" : 0
    }
  ]
}

Pattern Analyzer

语句:

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

结果:

{
  "tokens" : [
    {
      "token" : "the",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "2",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "quick",
      "start_offset" : 6,
      "end_offset" : 11,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "brown",
      "start_offset" : 12,
      "end_offset" : 17,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "foxes",
      "start_offset" : 18,
      "end_offset" : 23,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "jumped",
      "start_offset" : 24,
      "end_offset" : 30,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "over",
      "start_offset" : 31,
      "end_offset" : 35,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "the",
      "start_offset" : 36,
      "end_offset" : 39,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "lazy",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "dog",
      "start_offset" : 45,
      "end_offset" : 48,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "s",
      "start_offset" : 49,
      "end_offset" : 50,
      "type" : "word",
      "position" : 10
    },
    {
      "token" : "bone",
      "start_offset" : 51,
      "end_offset" : 55,
      "type" : "word",
      "position" : 11
    }
  ]
}

自定义正则表达式:

PUT pattern-demo
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_email_analyzer": {
          "type":      "pattern",
          "pattern":   "\\W|_", 
          "lowercase": true
        }
      }
    }
  }
}

POST pattern-demo/_analyze
{
  "analyzer": "my_email_analyzer",
  "text": "John_Smith@foo-bar.com"
}

结果:

{
  "tokens" : [
    {
      "token" : "john",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "smith",
      "start_offset" : 5,
      "end_offset" : 10,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "foo",
      "start_offset" : 11,
      "end_offset" : 14,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "bar",
      "start_offset" : 15,
      "end_offset" : 18,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "com",
      "start_offset" : 19,
      "end_offset" : 22,
      "type" : "word",
      "position" : 4
    }
  ]
}

Language Analyzer(English Analyzer)

语句:

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

结果:

{
  "tokens" : [
    {
      "token" : "2",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<NUM>",
      "position" : 1
    },
    {
      "token" : "quick",
      "start_offset" : 6,
      "end_offset" : 11,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "brown",
      "start_offset" : 12,
      "end_offset" : 17,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "fox",
      "start_offset" : 18,
      "end_offset" : 23,
      "type" : "<ALPHANUM>",
      "position" : 4
    },
    {
      "token" : "jump",
      "start_offset" : 24,
      "end_offset" : 30,
      "type" : "<ALPHANUM>",
      "position" : 5
    },
    {
      "token" : "over",
      "start_offset" : 31,
      "end_offset" : 35,
      "type" : "<ALPHANUM>",
      "position" : 6
    },
    {
      "token" : "lazi",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "<ALPHANUM>",
      "position" : 8
    },
    {
      "token" : "dog",
      "start_offset" : 45,
      "end_offset" : 50,
      "type" : "<ALPHANUM>",
      "position" : 9
    },
    {
      "token" : "bone",
      "start_offset" : 51,
      "end_offset" : 55,
      "type" : "<ALPHANUM>",
      "position" : 10
    }
  ]
}

ICU Analyzer

语句:

POST _analyze
{
  "analyzer": "icu_analyzer",
  "text": "他说的确实在理"
}

结果:

{
  "tokens" : [
    {
      "token" : "他",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "说的",
      "start_offset" : 1,
      "end_offset" : 3,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "确实",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "在",
      "start_offset" : 5,
      "end_offset" : 6,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    },
    {
      "token" : "理",
      "start_offset" : 6,
      "end_offset" : 7,
      "type" : "<IDEOGRAPHIC>",
      "position" : 4
    }
  ]
}

参考资料

©著作权归作者所有,转载或内容合作请联系作者
  • 序言:七十年代末,一起剥皮案震惊了整个滨河市,随后出现的几起案子,更是在滨河造成了极大的恐慌,老刑警刘岩,带你破解...
    沈念sama阅读 206,723评论 6 481
  • 序言:滨河连续发生了三起死亡事件,死亡现场离奇诡异,居然都是意外死亡,警方通过查阅死者的电脑和手机,发现死者居然都...
    沈念sama阅读 88,485评论 2 382
  • 文/潘晓璐 我一进店门,熙熙楼的掌柜王于贵愁眉苦脸地迎上来,“玉大人,你说我怎么就摊上这事。” “怎么了?”我有些...
    开封第一讲书人阅读 152,998评论 0 344
  • 文/不坏的土叔 我叫张陵,是天一观的道长。 经常有香客问我,道长,这世上最难降的妖魔是什么? 我笑而不...
    开封第一讲书人阅读 55,323评论 1 279
  • 正文 为了忘掉前任,我火速办了婚礼,结果婚礼上,老公的妹妹穿的比我还像新娘。我一直安慰自己,他们只是感情好,可当我...
    茶点故事阅读 64,355评论 5 374
  • 文/花漫 我一把揭开白布。 她就那样静静地躺着,像睡着了一般。 火红的嫁衣衬着肌肤如雪。 梳的纹丝不乱的头发上,一...
    开封第一讲书人阅读 49,079评论 1 285
  • 那天,我揣着相机与录音,去河边找鬼。 笑死,一个胖子当着我的面吹牛,可吹牛的内容都是我干的。 我是一名探鬼主播,决...
    沈念sama阅读 38,389评论 3 400
  • 文/苍兰香墨 我猛地睁开眼,长吁一口气:“原来是场噩梦啊……” “哼!你这毒妇竟也来了?” 一声冷哼从身侧响起,我...
    开封第一讲书人阅读 37,019评论 0 259
  • 序言:老挝万荣一对情侣失踪,失踪者是张志新(化名)和其女友刘颖,没想到半个月后,有当地人在树林里发现了一具尸体,经...
    沈念sama阅读 43,519评论 1 300
  • 正文 独居荒郊野岭守林人离奇死亡,尸身上长有42处带血的脓包…… 初始之章·张勋 以下内容为张勋视角 年9月15日...
    茶点故事阅读 35,971评论 2 325
  • 正文 我和宋清朗相恋三年,在试婚纱的时候发现自己被绿了。 大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
    茶点故事阅读 38,100评论 1 333
  • 序言:一个原本活蹦乱跳的男人离奇死亡,死状恐怖,灵堂内的尸体忽然破棺而出,到底是诈尸还是另有隐情,我是刑警宁泽,带...
    沈念sama阅读 33,738评论 4 324
  • 正文 年R本政府宣布,位于F岛的核电站,受9级特大地震影响,放射性物质发生泄漏。R本人自食恶果不足惜,却给世界环境...
    茶点故事阅读 39,293评论 3 307
  • 文/蒙蒙 一、第九天 我趴在偏房一处隐蔽的房顶上张望。 院中可真热闹,春花似锦、人声如沸。这庄子的主人今日做“春日...
    开封第一讲书人阅读 30,289评论 0 19
  • 文/苍兰香墨 我抬头看了看天上的太阳。三九已至,却和暖如春,着一层夹袄步出监牢的瞬间,已是汗流浃背。 一阵脚步声响...
    开封第一讲书人阅读 31,517评论 1 262
  • 我被黑心中介骗来泰国打工, 没想到刚下飞机就差点儿被人妖公主榨干…… 1. 我叫王不留,地道东北人。 一个月前我还...
    沈念sama阅读 45,547评论 2 354
  • 正文 我出身青楼,却偏偏与公主长得像,于是被迫代替她去往敌国和亲。 传闻我的和亲对象是个残疾皇子,可洞房花烛夜当晚...
    茶点故事阅读 42,834评论 2 345

推荐阅读更多精彩内容