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分词器

1 normalization：文档规范化,提高召回率

#normalization
GET _analyze
{"text": "Mr. Ma is an excellent teacher","analyzer": "english"
}

2 字符过滤器（character filter）：分词之前的预处理，过滤无用字符

• HTML Strip Character Filter：html_strip
  • 参数：escaped_tags 需要保留的html标签

##HTML Strip Character Filter
###测试数据<p>I&apos;m so <a>happy</a>!</p>
DELETE my_index
PUT my_index
{"settings": {"analysis": {"char_filter": {"my_char_filter":{"type":"html_strip","escaped_tags":["a"]}},"analyzer": {"my_analyzer":{"tokenizer":"keyword","char_filter":["my_char_filter"]}}}}
}

• Mapping Character Filter：type mapping

##Mapping Character Filter 
DELETE my_index
PUT my_index
{"settings": {"analysis": {"char_filter": {"my_char_filter":{"type":"mapping","mappings":["滚 => *","垃 => *","圾 => *"]}},"analyzer": {"my_analyzer":{"tokenizer":"keyword","char_filter":["my_char_filter"]}}}}
}
GET my_index/_analyze
{"analyzer": "my_analyzer","text": "你就是个垃圾！滚"
}

• Pattern Replace Character Filter：type pattern_replace

##Pattern Replace Character Filter 
#17611001200
DELETE my_index
PUT my_index
{"settings": {"analysis": {"char_filter": {"my_char_filter":{"type":"pattern_replace","pattern":"(\\d{3})\\d{4}(\\d{4})","replacement":"$1****$2"}},"analyzer": {"my_analyzer":{"tokenizer":"keyword","char_filter":["my_char_filter"]}}}}
}
GET my_index/_analyze
{"analyzer": "my_analyzer","text": "您的手机号是17611001200"
}

3 令牌过滤器（token filter）：停用词、时态转换、大小写转换、同义词转换、语气词处理等。比如：has=>have him=>he apples=>apple the/oh/a=>干掉

#token filter
DELETE test_index
PUT /test_index
{"settings": {"analysis": {"filter": {"my_synonym": {"type": "synonym_graph","synonyms_path": "analysis/synonym.txt"}},"analyzer": {"my_analyzer": {"tokenizer": "ik_max_word","filter": [ "my_synonym" ]}}}}
}
GET test_index/_analyze
{"analyzer": "my_analyzer","text": ["蒙丢丢，大G，霸道，daG"]
}
GET test_index/_analyze
{"analyzer": "ik_max_word","text": ["奔驰G级"]
}
DELETE test_index
PUT /test_index
{"settings": {"analysis": {"filter": {"my_synonym": {"type": "sys","synonyms": ["赵,钱,孙,李=>吴","周=>王"]}},"analyzer": {"my_analyzer": {"tokenizer": "standard","filter": [ "my_synonym" ]}}}}
}
GET test_index/_analyze
{"analyzer": "my_analyzer","text": ["赵,钱,孙,李","周"]
}
#大小写
GET test_index/_analyze
{"tokenizer": "standard","filter": ["lowercase"], "text": ["AASD ASDA SDASD ASDASD"]
}
GET test_index/_analyze
{"tokenizer": "standard","filter": ["uppercase"], "text": ["asdasd asd asg dsfg gfhjsdf asfdg g"]
}GET test_index/_analyze
{"tokenizer": "standard","filter": {"type": "condition","filter":"uppercase","script": {"source": "token.getTerm().length() < 5"}}, "text": ["asdasd asd asg dsfg gfhjsdf asfdg g"]
}
#停用词
DELETE test_index
PUT /test_index
{"settings": {"analysis": {"analyzer": {"my_analyzer": {"type": "standard","stopwords":["me","you"]}}}}
}
GET test_index/_analyze
{"analyzer": "my_analyzer", "text": ["Teacher me and you in the china"]
}

4 分词器（tokenizer）：切词

#分词器 tokenizer
GET test_index/_analyze
{"tokenizer": "ik_max_word","text": ["我爱北京天安门","天安门上太阳升"]
}

5 常见分词器：

• standard analyzer：默认分词器，中文支持的不理想，会逐字拆分。
• pattern tokenizer：以正则匹配分隔符，把文本拆分成若干词项。
• simple pattern tokenizer：以正则匹配词项，速度比pattern tokenizer快。
• whitespace analyzer：以空白符分隔 Tim_cookie

6 自定义分词器：custom analyzer

• char_filter：内置或自定义字符过滤器 。
• token filter：内置或自定义token filter 。
• tokenizer：内置或自定义切词器。

#自定义分词器
DELETE custom_analysis
PUT custom_analysis
{"settings": {"analysis": {"char_filter": {"my_char_filter": {"type": "mapping","mappings": ["& => and","| => or"]},"html_strip_char_filter":{"type":"html_strip","escaped_tags":["a"]}},"filter": {"my_stopword": {"type": "stop","stopwords": ["is","in","the","a","at","for"]}},"tokenizer": {"my_tokenizer": {"type": "pattern","pattern": "[ ,.!?]"}}, "analyzer": {"my_analyzer":{"type":"custom","char_filter":["my_char_filter","html_strip_char_filter"],"filter":["my_stopword","lowercase"],"tokenizer":"my_tokenizer"}}}}
}GET custom_analysis/_analyze
{"analyzer": "my_analyzer","text": ["What is ,<a>as.df</a>  ss<p> in ? &</p> | is ! in the a at for "]
}

7 中文分词器：ik分词

1. 安装和部署

   • ik下载地址：https://github.com/medcl/elasticsearch-analysis-ik
   • Github加速器：https://github.com/fhefh2015/Fast-GitHub
   • 创建插件文件夹 cd your-es-root/plugins/ && mkdir ik
   • 将插件解压缩到文件夹 your-es-root/plugins/ik
   • 重新启动es

2. IK文件描述

   • IKAnalyzer.cfg.xml：IK分词配置文件

• 主词库：main.dic
  • 英文停用词：stopword.dic，不会建立在倒排索引中
  • 特殊词库：
    • quantifier.dic：特殊词库：计量单位等
    • suffix.dic：特殊词库：行政单位
    • surname.dic：特殊词库：百家姓
    • preposition：特殊词库：语气词
  • 自定义词库：网络词汇、流行词、自造词等

3. ik提供的两种analyzer:

   1. ik_max_word会将文本做最细粒度的拆分，比如会将“中华人民共和国国歌”拆分为“中华人民共和国,中华人民,中华,华人,人民共和国,人民,人,民,共和国,共和,和,国国,国歌”，会穷尽各种可能的组合，适合 Term Query；
   2. ik_smart: 会做最粗粒度的拆分，比如会将“中华人民共和国国歌”拆分为“中华人民共和国,国歌”，适合 Phrase 查询。

4. 热更新

   1. 远程词库文件
      1. 优点：上手简单
      2. 缺点：
         1. 词库的管理不方便，要操作直接操作磁盘文件，检索页很麻烦
         2. 文件的读写没有专门的优化性能不好
         3. 多一层接口调用和网络传输
   2. ik访问数据库
      1. MySQL驱动版本兼容性
         1. https://dev.mysql.com/doc/connector-j/8.0/en/connector-j-versions.html
         2. https://dev.mysql.com/doc/connector-j/5.1/en/connector-j-versions.html
      2. 驱动下载地址
         1. https://mvnrepository.com/artifact/mysql/mysql-connector-java

GET custom_analysis/_analyze
{"analyzer": "ik_max_word","text": ["我爱中华人民共和国"]
}GET custom_analysis/_analyze
{"analyzer": "ik_max_word","text": ["蒙丢丢","大G","霸道","渣男","渣女","奥巴马"]
}GET custom_analysis/_analyze
{"analyzer": "ik_max_word","text": ["吴磊","美国","日本","澳大利亚"]
}