带短语匹配的边缘NGram

Question

我需要自动完成词组。例如，当我搜索“阿尔茨海默症中的痴呆”时，我想得到“老年痴呆症”。

为此，我配置了边缘NGram令牌器。我尝试使用edge_ngram_analyzer和standard作为查询体中的分析器。然而，当我试图匹配一个短语时，我无法得到结果。

我做错了什么？

我的问题是：

{
  "query":{
    "multi_match":{
      "query":"dementia in alz",
      "type":"phrase",
      "analyzer":"edge_ngram_analyzer",
      "fields":["_all"]
    }
  }
}

我的映射：

...
"type" : {
  "_all" : {
    "analyzer" : "edge_ngram_analyzer",
    "search_analyzer" : "standard"
  },
  "properties" : {
    "field" : {
      "type" : "string",
      "analyzer" : "edge_ngram_analyzer",
      "search_analyzer" : "standard"
    },
...
"settings" : {
  ...
  "analysis" : {
    "filter" : {
      "stem_possessive_filter" : {
        "name" : "possessive_english",
        "type" : "stemmer"
      }
    },
    "analyzer" : {
      "edge_ngram_analyzer" : {
        "filter" : [ "lowercase" ],
        "tokenizer" : "edge_ngram_tokenizer"
      }
    },
    "tokenizer" : {
      "edge_ngram_tokenizer" : {
        "token_chars" : [ "letter", "digit", "whitespace" ],
        "min_gram" : "2",
        "type" : "edgeNGram",
        "max_gram" : "25"
      }
    }
  }
  ...

我的文件：

{
  "_score": 1.1152233, 
  "_type": "Diagnosis", 
  "_id": "AVZLfHfBE5CzEm8aJ3Xp", 
  "_source": {
    "@timestamp": "2016-08-02T13:40:48.665Z", 
    "type": "Diagnosis", 
    "Document_ID": "Diagnosis_1400541", 
    "Diagnosis": "F00.0 -  Dementia in Alzheimer's disease with early onset", 
    "@version": "1", 
  }, 
  "_index": "carenotes"
}, 
{
  "_score": 1.1152233, 
  "_type": "Diagnosis", 
  "_id": "AVZLfICrE5CzEm8aJ4Dc", 
  "_source": {
    "@timestamp": "2016-08-02T13:40:51.240Z", 
    "type": "Diagnosis", 
    "Document_ID": "Diagnosis_1424351", 
    "Diagnosis": "F00.1 -  Dementia in Alzheimer's disease with late onset", 
    "@version": "1", 
  }, 
  "_index": "carenotes"
}

“老年痴呆症”短语分析：

{
  "tokens": [
    {
      "end_offset": 2, 
      "token": "de", 
      "type": "word", 
      "start_offset": 0, 
      "position": 0
    }, 
    {
      "end_offset": 3, 
      "token": "dem", 
      "type": "word", 
      "start_offset": 0, 
      "position": 1
    }, 
    {
      "end_offset": 4, 
      "token": "deme", 
      "type": "word", 
      "start_offset": 0, 
      "position": 2
    }, 
    {
      "end_offset": 5, 
      "token": "demen", 
      "type": "word", 
      "start_offset": 0, 
      "position": 3
    }, 
    {
      "end_offset": 6, 
      "token": "dement", 
      "type": "word", 
      "start_offset": 0, 
      "position": 4
    }, 
    {
      "end_offset": 7, 
      "token": "dementi", 
      "type": "word", 
      "start_offset": 0, 
      "position": 5
    }, 
    {
      "end_offset": 8, 
      "token": "dementia", 
      "type": "word", 
      "start_offset": 0, 
      "position": 6
    }, 
    {
      "end_offset": 9, 
      "token": "dementia ", 
      "type": "word", 
      "start_offset": 0, 
      "position": 7
    }, 
    {
      "end_offset": 10, 
      "token": "dementia i", 
      "type": "word", 
      "start_offset": 0, 
      "position": 8
    }, 
    {
      "end_offset": 11, 
      "token": "dementia in", 
      "type": "word", 
      "start_offset": 0, 
      "position": 9
    }, 
    {
      "end_offset": 12, 
      "token": "dementia in ", 
      "type": "word", 
      "start_offset": 0, 
      "position": 10
    }, 
    {
      "end_offset": 13, 
      "token": "dementia in a", 
      "type": "word", 
      "start_offset": 0, 
      "position": 11
    }, 
    {
      "end_offset": 14, 
      "token": "dementia in al", 
      "type": "word", 
      "start_offset": 0, 
      "position": 12
    }, 
    {
      "end_offset": 15, 
      "token": "dementia in alz", 
      "type": "word", 
      "start_offset": 0, 
      "position": 13
    }, 
    {
      "end_offset": 16, 
      "token": "dementia in alzh", 
      "type": "word", 
      "start_offset": 0, 
      "position": 14
    }, 
    {
      "end_offset": 17, 
      "token": "dementia in alzhe", 
      "type": "word", 
      "start_offset": 0, 
      "position": 15
    }, 
    {
      "end_offset": 18, 
      "token": "dementia in alzhei", 
      "type": "word", 
      "start_offset": 0, 
      "position": 16
    }, 
    {
      "end_offset": 19, 
      "token": "dementia in alzheim", 
      "type": "word", 
      "start_offset": 0, 
      "position": 17
    }, 
    {
      "end_offset": 20, 
      "token": "dementia in alzheime", 
      "type": "word", 
      "start_offset": 0, 
      "position": 18
    }, 
    {
      "end_offset": 21, 
      "token": "dementia in alzheimer", 
      "type": "word", 
      "start_offset": 0, 
      "position": 19
    }
  ]
}

Answer 1

非常感谢伦德尔帮助我找到了正确的解决方案！

Andrei Stefan的解不是最优解。

为什么？首先，搜索分析器中缺少小写过滤器，这使得搜索变得不方便；大小写必须严格匹配。需要一个带有lowercase过滤器的自定义分析器，而不是"analyzer": "keyword"。

第二，分析部分错了，！在索引时间内，edge_ngram_analyzer分析了一串“早发阿尔茨海默病的F00.0-痴呆”。使用此分析器，我们有以下字典数组作为分析字符串：

{
  "tokens": [
    {
      "end_offset": 2, 
      "token": "f0", 
      "type": "word", 
      "start_offset": 0, 
      "position": 0
    }, 
    {
      "end_offset": 3, 
      "token": "f00", 
      "type": "word", 
      "start_offset": 0, 
      "position": 1
    }, 
    {
      "end_offset": 6, 
      "token": "0 ", 
      "type": "word", 
      "start_offset": 4, 
      "position": 2
    }, 
    {
      "end_offset": 9, 
      "token": "  ", 
      "type": "word", 
      "start_offset": 7, 
      "position": 3
    }, 
    {
      "end_offset": 10, 
      "token": "  d", 
      "type": "word", 
      "start_offset": 7, 
      "position": 4
    }, 
    {
      "end_offset": 11, 
      "token": "  de", 
      "type": "word", 
      "start_offset": 7, 
      "position": 5
    }, 
    {
      "end_offset": 12, 
      "token": "  dem", 
      "type": "word", 
      "start_offset": 7, 
      "position": 6
    }, 
    {
      "end_offset": 13, 
      "token": "  deme", 
      "type": "word", 
      "start_offset": 7, 
      "position": 7
    }, 
    {
      "end_offset": 14, 
      "token": "  demen", 
      "type": "word", 
      "start_offset": 7, 
      "position": 8
    }, 
    {
      "end_offset": 15, 
      "token": "  dement", 
      "type": "word", 
      "start_offset": 7, 
      "position": 9
    }, 
    {
      "end_offset": 16, 
      "token": "  dementi", 
      "type": "word", 
      "start_offset": 7, 
      "position": 10
    }, 
    {
      "end_offset": 17, 
      "token": "  dementia", 
      "type": "word", 
      "start_offset": 7, 
      "position": 11
    }, 
    {
      "end_offset": 18, 
      "token": "  dementia ", 
      "type": "word", 
      "start_offset": 7, 
      "position": 12
    }, 
    {
      "end_offset": 19, 
      "token": "  dementia i", 
      "type": "word", 
      "start_offset": 7, 
      "position": 13
    }, 
    {
      "end_offset": 20, 
      "token": "  dementia in", 
      "type": "word", 
      "start_offset": 7, 
      "position": 14
    }, 
    {
      "end_offset": 21, 
      "token": "  dementia in ", 
      "type": "word", 
      "start_offset": 7, 
      "position": 15
    }, 
    {
      "end_offset": 22, 
      "token": "  dementia in a", 
      "type": "word", 
      "start_offset": 7, 
      "position": 16
    }, 
    {
      "end_offset": 23, 
      "token": "  dementia in al", 
      "type": "word", 
      "start_offset": 7, 
      "position": 17
    }, 
    {
      "end_offset": 24, 
      "token": "  dementia in alz", 
      "type": "word", 
      "start_offset": 7, 
      "position": 18
    }, 
    {
      "end_offset": 25, 
      "token": "  dementia in alzh", 
      "type": "word", 
      "start_offset": 7, 
      "position": 19
    }, 
    {
      "end_offset": 26, 
      "token": "  dementia in alzhe", 
      "type": "word", 
      "start_offset": 7, 
      "position": 20
    }, 
    {
      "end_offset": 27, 
      "token": "  dementia in alzhei", 
      "type": "word", 
      "start_offset": 7, 
      "position": 21
    }, 
    {
      "end_offset": 28, 
      "token": "  dementia in alzheim", 
      "type": "word", 
      "start_offset": 7, 
      "position": 22
    }, 
    {
      "end_offset": 29, 
      "token": "  dementia in alzheime", 
      "type": "word", 
      "start_offset": 7, 
      "position": 23
    }, 
    {
      "end_offset": 30, 
      "token": "  dementia in alzheimer", 
      "type": "word", 
      "start_offset": 7, 
      "position": 24
    }, 
    {
      "end_offset": 33, 
      "token": "s ", 
      "type": "word", 
      "start_offset": 31, 
      "position": 25
    }, 
    {
      "end_offset": 34, 
      "token": "s d", 
      "type": "word", 
      "start_offset": 31, 
      "position": 26
    }, 
    {
      "end_offset": 35, 
      "token": "s di", 
      "type": "word", 
      "start_offset": 31, 
      "position": 27
    }, 
    {
      "end_offset": 36, 
      "token": "s dis", 
      "type": "word", 
      "start_offset": 31, 
      "position": 28
    }, 
    {
      "end_offset": 37, 
      "token": "s dise", 
      "type": "word", 
      "start_offset": 31, 
      "position": 29
    }, 
    {
      "end_offset": 38, 
      "token": "s disea", 
      "type": "word", 
      "start_offset": 31, 
      "position": 30
    }, 
    {
      "end_offset": 39, 
      "token": "s diseas", 
      "type": "word", 
      "start_offset": 31, 
      "position": 31
    }, 
    {
      "end_offset": 40, 
      "token": "s disease", 
      "type": "word", 
      "start_offset": 31, 
      "position": 32
    }, 
    {
      "end_offset": 41, 
      "token": "s disease ", 
      "type": "word", 
      "start_offset": 31, 
      "position": 33
    }, 
    {
      "end_offset": 42, 
      "token": "s disease w", 
      "type": "word", 
      "start_offset": 31, 
      "position": 34
    }, 
    {
      "end_offset": 43, 
      "token": "s disease wi", 
      "type": "word", 
      "start_offset": 31, 
      "position": 35
    }, 
    {
      "end_offset": 44, 
      "token": "s disease wit", 
      "type": "word", 
      "start_offset": 31, 
      "position": 36
    }, 
    {
      "end_offset": 45, 
      "token": "s disease with", 
      "type": "word", 
      "start_offset": 31, 
      "position": 37
    }, 
    {
      "end_offset": 46, 
      "token": "s disease with ", 
      "type": "word", 
      "start_offset": 31, 
      "position": 38
    }, 
    {
      "end_offset": 47, 
      "token": "s disease with e", 
      "type": "word", 
      "start_offset": 31, 
      "position": 39
    }, 
    {
      "end_offset": 48, 
      "token": "s disease with ea", 
      "type": "word", 
      "start_offset": 31, 
      "position": 40
    }, 
    {
      "end_offset": 49, 
      "token": "s disease with ear", 
      "type": "word", 
      "start_offset": 31, 
      "position": 41
    }, 
    {
      "end_offset": 50, 
      "token": "s disease with earl", 
      "type": "word", 
      "start_offset": 31, 
      "position": 42
    }, 
    {
      "end_offset": 51, 
      "token": "s disease with early", 
      "type": "word", 
      "start_offset": 31, 
      "position": 43
    }, 
    {
      "end_offset": 52, 
      "token": "s disease with early ", 
      "type": "word", 
      "start_offset": 31, 
      "position": 44
    }, 
    {
      "end_offset": 53, 
      "token": "s disease with early o", 
      "type": "word", 
      "start_offset": 31, 
      "position": 45
    }, 
    {
      "end_offset": 54, 
      "token": "s disease with early on", 
      "type": "word", 
      "start_offset": 31, 
      "position": 46
    }, 
    {
      "end_offset": 55, 
      "token": "s disease with early ons", 
      "type": "word", 
      "start_offset": 31, 
      "position": 47
    }, 
    {
      "end_offset": 56, 
      "token": "s disease with early onse", 
      "type": "word", 
      "start_offset": 31, 
      "position": 48
    }
  ]
}

如您所见，整个字符串标记的标记大小为2到25个字符。字符串以线性方式标记，每个新令牌的所有空格和位置都会增加一个。

它有几个问题：

1. edge_ngram_analyzer生成的无用标记永远不会被搜索，例如：“”、“”、“d”、"s d“、"s disease w”等等。
2. 而且，--它没有产生很多有用的标记，例如：“疾病”、“早发”等等。如果你试图搜索这些单词，就会有0的结果。
3. 注意，最后一个标记是“早期的疾病”。最后的"t“在哪里？由于"max_gram" : "25"，我们在所有字段中都有一些文本“lost”。您不能再搜索这个文本了，因为它没有标记。
4. trim过滤器只在可以由令牌程序完成时混淆问题，过滤额外的空间。
5. edge_ngram_analyzer增加每个令牌的位置，这对于诸如短语查询之类的位置查询是有问题的。我们应该使用edge_ngram_filter，这样在生成ngram时，将保留令牌的位置。

最优解

要使用的映射设置：

...
"mappings": {
    "Type": {
       "_all":{
          "analyzer": "edge_ngram_analyzer", 
          "search_analyzer": "keyword_analyzer"
        }, 
        "properties": {
          "Field": {
            "search_analyzer": "keyword_analyzer",
             "type": "string",
             "analyzer": "edge_ngram_analyzer"
          },
...
...
"settings": {
   "analysis": {
      "filter": {
         "english_poss_stemmer": {
            "type": "stemmer",
            "name": "possessive_english"
         },
         "edge_ngram": {
           "type": "edgeNGram",
           "min_gram": "2",
           "max_gram": "25",
           "token_chars": ["letter", "digit"]
         }
      },
      "analyzer": {
         "edge_ngram_analyzer": {
           "filter": ["lowercase", "english_poss_stemmer", "edge_ngram"],
           "tokenizer": "standard"
         },
         "keyword_analyzer": {
           "filter": ["lowercase", "english_poss_stemmer"],
           "tokenizer": "standard"
         }
      }
   }
}
...

看一看分析：

{
  "tokens": [
    {
      "end_offset": 5, 
      "token": "f0", 
      "type": "word", 
      "start_offset": 0, 
      "position": 0
    }, 
    {
      "end_offset": 5, 
      "token": "f00", 
      "type": "word", 
      "start_offset": 0, 
      "position": 0
    }, 
    {
      "end_offset": 5, 
      "token": "f00.", 
      "type": "word", 
      "start_offset": 0, 
      "position": 0
    }, 
    {
      "end_offset": 5, 
      "token": "f00.0", 
      "type": "word", 
      "start_offset": 0, 
      "position": 0
    }, 
    {
      "end_offset": 17, 
      "token": "de", 
      "type": "word", 
      "start_offset": 9, 
      "position": 2
    }, 
    {
      "end_offset": 17, 
      "token": "dem", 
      "type": "word", 
      "start_offset": 9, 
      "position": 2
    }, 
    {
      "end_offset": 17, 
      "token": "deme", 
      "type": "word", 
      "start_offset": 9, 
      "position": 2
    }, 
    {
      "end_offset": 17, 
      "token": "demen", 
      "type": "word", 
      "start_offset": 9, 
      "position": 2
    }, 
    {
      "end_offset": 17, 
      "token": "dement", 
      "type": "word", 
      "start_offset": 9, 
      "position": 2
    }, 
    {
      "end_offset": 17, 
      "token": "dementi", 
      "type": "word", 
      "start_offset": 9, 
      "position": 2
    }, 
    {
      "end_offset": 17, 
      "token": "dementia", 
      "type": "word", 
      "start_offset": 9, 
      "position": 2
    }, 
    {
      "end_offset": 20, 
      "token": "in", 
      "type": "word", 
      "start_offset": 18, 
      "position": 3
    }, 
    {
      "end_offset": 32, 
      "token": "al", 
      "type": "word", 
      "start_offset": 21, 
      "position": 4
    }, 
    {
      "end_offset": 32, 
      "token": "alz", 
      "type": "word", 
      "start_offset": 21, 
      "position": 4
    }, 
    {
      "end_offset": 32, 
      "token": "alzh", 
      "type": "word", 
      "start_offset": 21, 
      "position": 4
    }, 
    {
      "end_offset": 32, 
      "token": "alzhe", 
      "type": "word", 
      "start_offset": 21, 
      "position": 4
    }, 
    {
      "end_offset": 32, 
      "token": "alzhei", 
      "type": "word", 
      "start_offset": 21, 
      "position": 4
    }, 
    {
      "end_offset": 32, 
      "token": "alzheim", 
      "type": "word", 
      "start_offset": 21, 
      "position": 4
    }, 
    {
      "end_offset": 32, 
      "token": "alzheime", 
      "type": "word", 
      "start_offset": 21, 
      "position": 4
    }, 
    {
      "end_offset": 32, 
      "token": "alzheimer", 
      "type": "word", 
      "start_offset": 21, 
      "position": 4
    }, 
    {
      "end_offset": 40, 
      "token": "di", 
      "type": "word", 
      "start_offset": 33, 
      "position": 5
    }, 
    {
      "end_offset": 40, 
      "token": "dis", 
      "type": "word", 
      "start_offset": 33, 
      "position": 5
    }, 
    {
      "end_offset": 40, 
      "token": "dise", 
      "type": "word", 
      "start_offset": 33, 
      "position": 5
    }, 
    {
      "end_offset": 40, 
      "token": "disea", 
      "type": "word", 
      "start_offset": 33, 
      "position": 5
    }, 
    {
      "end_offset": 40, 
      "token": "diseas", 
      "type": "word", 
      "start_offset": 33, 
      "position": 5
    }, 
    {
      "end_offset": 40, 
      "token": "disease", 
      "type": "word", 
      "start_offset": 33, 
      "position": 5
    }, 
    {
      "end_offset": 45, 
      "token": "wi", 
      "type": "word", 
      "start_offset": 41, 
      "position": 6
    }, 
    {
      "end_offset": 45, 
      "token": "wit", 
      "type": "word", 
      "start_offset": 41, 
      "position": 6
    }, 
    {
      "end_offset": 45, 
      "token": "with", 
      "type": "word", 
      "start_offset": 41, 
      "position": 6
    }, 
    {
      "end_offset": 51, 
      "token": "ea", 
      "type": "word", 
      "start_offset": 46, 
      "position": 7
    }, 
    {
      "end_offset": 51, 
      "token": "ear", 
      "type": "word", 
      "start_offset": 46, 
      "position": 7
    }, 
    {
      "end_offset": 51, 
      "token": "earl", 
      "type": "word", 
      "start_offset": 46, 
      "position": 7
    }, 
    {
      "end_offset": 51, 
      "token": "early", 
      "type": "word", 
      "start_offset": 46, 
      "position": 7
    }, 
    {
      "end_offset": 57, 
      "token": "on", 
      "type": "word", 
      "start_offset": 52, 
      "position": 8
    }, 
    {
      "end_offset": 57, 
      "token": "ons", 
      "type": "word", 
      "start_offset": 52, 
      "position": 8
    }, 
    {
      "end_offset": 57, 
      "token": "onse", 
      "type": "word", 
      "start_offset": 52, 
      "position": 8
    }, 
    {
      "end_offset": 57, 
      "token": "onset", 
      "type": "word", 
      "start_offset": 52, 
      "position": 8
    }
  ]
}

在索引时间，文本被standard标记器标记化，然后由lowercase、possessive_english和edge_ngram过滤器过滤单独的单词。标记只为单词生成。在搜索时间，文本被standard标记器标记化，然后由lowercase和possessive_english对单独的单词进行过滤。搜索的单词与索引时间内创建的标记相匹配。

因此，我们使增量搜索成为可能！

现在，由于我们对单独的单词执行ngram，我们甚至可以执行如下的查询

{
  'query': {
    'multi_match': {
      'query': 'dem in alzh',  
      'type': 'phrase', 
      'fields': ['_all']
    }
  }
}

得到正确的结果。

没有文本是“丢失”的，一切都是可搜索的，并且不再需要使用trim过滤器来处理空格。

Answer 2

我认为您的查询是错误的:虽然在索引时需要nGrams，但在搜索时不需要它们。在搜索时，您需要文本尽可能“固定”。请尝试以下查询：

{
  "query": {
    "multi_match": {
      "query": "  dementia in alz",
      "analyzer": "keyword",
      "fields": [
        "_all"
      ]
    }
  }
}

您注意到在dementia之前有两个空白空间。这些都是由你的分析器从文本。为了摆脱那些你需要的trim token_filter：

   "edge_ngram_analyzer": {
      "filter": [
        "lowercase","trim"
      ],
      "tokenizer": "edge_ngram_tokenizer"
    }

然后这个查询就可以工作了(在dementia之前没有空格)：

{
  "query": {
    "multi_match": {
      "query": "dementia in alz",
      "analyzer": "keyword",
      "fields": [
        "_all"
      ]
    }
  }
}