使用Elastic search从文本中提取关键字（多词）

Question

我有一个充满关键字的索引，根据这些关键字，我想从输入文本中提取关键字。

以下是示例关键字索引。请注意，关键字也可以是多个单词，或者基本上是唯一的标签。

{
  "hits": {
    "total": 2000,
    "hits": [
      {
        "id": 1,
        "keyword": "thousand eyes"
      },
      {
        "id": 2,
        "keyword": "facebook"
      },
      {
        "id": 3,
        "keyword": "superdoc"
      },
      {
        "id": 4,
        "keyword": "quora"
      },
      {
        "id": 5,
        "keyword": "your story"
      },
      {
        "id": 6,
        "keyword": "Surgery"
      },
      {
        "id": 7,
        "keyword": "lending club"
      },
      {
        "id": 8,
        "keyword": "ad roll"
      },
      {
        "id": 9,
        "keyword": "the honest company"
      },
      {
        "id": 10,
        "keyword": "Draft kings"
      }
    ]
  }
}

现在，如果输入文本为 “我在Facebook上看到了借贷俱乐部的新闻，您的故事和法定人数” ，则搜索结果应为 [“借贷俱乐部”，“
facebook”，“您的故事”，“法定人数”]
。此外，搜索应 区分大小写

Answer 1

只有一种真正的方法可以做到这一点。您必须将您的数据作为关键字建立索引，并使用带状疱疹对其进行分析：

看到这个复制品：

首先，我们将创建两个自定义分析器：keyword和shingles：

PUT test
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer_keyword": {
          "type": "custom",
          "tokenizer": "keyword",
          "filter": [
            "asciifolding",
            "lowercase"
          ]
        },
        "my_analyzer_shingle": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [
            "asciifolding",
            "lowercase",
            "shingle"
          ]
        }
      }
    }
  },
  "mappings": {
    "your_type": {
      "properties": {
        "keyword": {
          "type": "string",
          "index_analyzer": "my_analyzer_keyword",
          "search_analyzer": "my_analyzer_shingle"
        }
      }
    }
  }
}

现在，让我们使用您提供的数据创建一些示例数据：

POST /test/your_type/1
{
  "id": 1,
  "keyword": "thousand eyes"
}
POST /test/your_type/2
{
  "id": 2,
  "keyword": "facebook"
}
POST /test/your_type/3
{
  "id": 3,
  "keyword": "superdoc"
}
POST /test/your_type/4
{
  "id": 4,
  "keyword": "quora"
}
POST /test/your_type/5
{
  "id": 5,
  "keyword": "your story"
}
POST /test/your_type/6
{
  "id": 6,
  "keyword": "Surgery"
}
POST /test/your_type/7
{
  "id": 7,
  "keyword": "lending club"
}
POST /test/your_type/8
{
  "id": 8,
  "keyword": "ad roll"
}
POST /test/your_type/9
{
  "id": 9,
  "keyword": "the honest company"
}
POST /test/your_type/10
{
  "id": 10,
  "keyword": "Draft kings"
}

最后查询以运行搜索：

POST /test/your_type/_search
{
  "query": {
    "match": {
      "keyword": "I saw the news of lending club on facebook, your story and quora"
    }
  }
}

这是结果：

{
  "took": 6,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 4,
    "max_score": 0.009332742,
    "hits": [
      {
        "_index": "test",
        "_type": "your_type",
        "_id": "2",
        "_score": 0.009332742,
        "_source": {
          "id": 2,
          "keyword": "facebook"
        }
      },
      {
        "_index": "test",
        "_type": "your_type",
        "_id": "7",
        "_score": 0.009332742,
        "_source": {
          "id": 7,
          "keyword": "lending club"
        }
      },
      {
        "_index": "test",
        "_type": "your_type",
        "_id": "4",
        "_score": 0.009207102,
        "_source": {
          "id": 4,
          "keyword": "quora"
        }
      },
      {
        "_index": "test",
        "_type": "your_type",
        "_id": "5",
        "_score": 0.0014755741,
        "_source": {
          "id": 5,
          "keyword": "your story"
        }
      }
    ]
  }
}

那么它在幕后做什么？

1. 它将您的文档索引为整个关键字（它将整个字符串作为单个标记发出）。我还添加了asiifolding过滤器，因此它可以对字母进行规范化（即é成为e）和小写过滤器（不区分大小写的搜索）。因此例如Draft kings被索引为draft kings
2. 现在，搜索分析器使用的是相同的逻辑，除了它的令牌生成器发出单词令牌，并在其之上创建带状疱疹（令牌的组合）之外，它将匹配第一步中索引的关键字。