IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

« Simple pattern split tokenizer Thai tokenizer »

› › ›

Standard tokenizer

edit

Standard tokenizer

edit

The standard tokenizer 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

edit

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

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

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

POST _analyze
{
  "tokenizer": "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

edit

The standard tokenizer 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.

Example configuration

edit

In this example, we configure the standard tokenizer to have a max_token_length of 5 (for demonstration purposes):

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

resp1 = client.indices.analyze(
    index="my-index-000001",
    analyzer="my_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_analyzer: {
            tokenizer: 'my_tokenizer'
          }
        },
        tokenizer: {
          my_tokenizer: {
            type: 'standard',
            max_token_length: 5
          }
        }
      }
    }
  }
)
puts response

response = client.indices.analyze(
  index: 'my-index-000001',
  body: {
    analyzer: 'my_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_analyzer: {
          tokenizer: "my_tokenizer",
        },
      },
      tokenizer: {
        my_tokenizer: {
          type: "standard",
          max_token_length: 5,
        },
      },
    },
  },
});
console.log(response);

const response1 = await client.indices.analyze({
  index: "my-index-000001",
  analyzer: "my_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_analyzer": {
          "tokenizer": "my_tokenizer"
        }
      },
      "tokenizer": {
        "my_tokenizer": {
          "type": "standard",
          "max_token_length": 5
        }
      }
    }
  }
}

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

The above example produces the following terms:

[ The, 2, QUICK, Brown, Foxes, jumpe, d, over, the, lazy, dog's, bone ]

« Simple pattern split tokenizer Thai tokenizer »