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Standard analyzer

The standard analyzer is the default analyzer which is used if none is specified. It provides grammar based tokenization (based on the Unicode Text Segmentation algorithm, as specified in Unicode Standard Annex #29) and works well for most languages.

Example output

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resp = client.indices.analyze(
    analyzer="standard",
    text="The 2 QUICK Brown-Foxes jumped over the lazy dog's bone.",
)
print(resp)

response = client.indices.analyze(
  body: {
    analyzer: 'standard',
    text: "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
  }
)
puts response

const response = await client.indices.analyze({
  analyzer: "standard",
  text: "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone.",
});
console.log(response);

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

The above sentence would produce the following terms:

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

Configuration

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The standard analyzer accepts the following parameters:

`max_token_length`	The maximum token length. If a token is seen that exceeds this length then it is split at `max_token_length` intervals. Defaults to `255`.
`stopwords`	A pre-defined stop words list like `_english_` or an array containing a list of stop words. Defaults to `_none_`.
`stopwords_path`	The path to a file containing stop words.

See the Stop Token Filter for more information about stop word configuration.

Example configuration

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In this example, we configure the standard analyzer to have a max_token_length of 5 (for demonstration purposes), and to use the pre-defined list of English stop words:

resp = client.indices.create(
    index="my-index-000001",
    settings={
        "analysis": {
            "analyzer": {
                "my_english_analyzer": {
                    "type": "standard",
                    "max_token_length": 5,
                    "stopwords": "_english_"
                }
            }
        }
    },
)
print(resp)

resp1 = client.indices.analyze(
    index="my-index-000001",
    analyzer="my_english_analyzer",
    text="The 2 QUICK Brown-Foxes jumped over the lazy dog's bone.",
)
print(resp1)

response = client.indices.create(
  index: 'my-index-000001',
  body: {
    settings: {
      analysis: {
        analyzer: {
          my_english_analyzer: {
            type: 'standard',
            max_token_length: 5,
            stopwords: '_english_'
          }
        }
      }
    }
  }
)
puts response

response = client.indices.analyze(
  index: 'my-index-000001',
  body: {
    analyzer: 'my_english_analyzer',
    text: "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
  }
)
puts response

const response = await client.indices.create({
  index: "my-index-000001",
  settings: {
    analysis: {
      analyzer: {
        my_english_analyzer: {
          type: "standard",
          max_token_length: 5,
          stopwords: "_english_",
        },
      },
    },
  },
});
console.log(response);

const response1 = await client.indices.analyze({
  index: "my-index-000001",
  analyzer: "my_english_analyzer",
  text: "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone.",
});
console.log(response1);

PUT my-index-000001
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_english_analyzer": {
          "type": "standard",
          "max_token_length": 5,
          "stopwords": "_english_"
        }
      }
    }
  }
}

POST my-index-000001/_analyze
{
  "analyzer": "my_english_analyzer",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

The above example produces the following terms:

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

Definition

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The standard analyzer consists of:

Tokenizer

Standard Tokenizer

Token Filters

Lower Case Token Filter
Stop Token Filter (disabled by default)

If you need to customize the standard analyzer beyond the configuration parameters then you need to recreate it as a custom analyzer and modify it, usually by adding token filters. This would recreate the built-in standard analyzer and you can use it as a starting point:

resp = client.indices.create(
    index="standard_example",
    settings={
        "analysis": {
            "analyzer": {
                "rebuilt_standard": {
                    "tokenizer": "standard",
                    "filter": [
                        "lowercase"
                    ]
                }
            }
        }
    },
)
print(resp)

response = client.indices.create(
  index: 'standard_example',
  body: {
    settings: {
      analysis: {
        analyzer: {
          rebuilt_standard: {
            tokenizer: 'standard',
            filter: [
              'lowercase'
            ]
          }
        }
      }
    }
  }
)
puts response

const response = await client.indices.create({
  index: "standard_example",
  settings: {
    analysis: {
      analyzer: {
        rebuilt_standard: {
          tokenizer: "standard",
          filter: ["lowercase"],
        },
      },
    },
  },
});
console.log(response);

PUT /standard_example
{
  "settings": {
    "analysis": {
      "analyzer": {
        "rebuilt_standard": {
          "tokenizer": "standard",
          "filter": [
            "lowercase"       
          ]
        }
      }
    }
  }
}

You’d add any token filters after lowercase.

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