Tokenizers

By Ashwin Venugopal -

 





What are tokenizers ?

The part that separates the text into the tokens that will be kept in the index is called the tokenizer. Additionally, tokenizers deconstruct words into their most basic forms. Often, a token is a single word, but you might want to create unusual tokens such as:

  1. A full postal address.
  2. A complete URL or email address.
  3. Words based on the grammar of a specific language.

There are 13 different tokenizers to choose from. These tokenizers include:

  • classic- This tokenizer processes text based on grammar for European languages.

  • keyword- This tokenizer emits the entire input as a single token. Use this tokenizer for fields that should always be indexed as one value.

  • lowercase- This tokenizer divides text at non-letters and then modifies the resulting tokens to all lower case.

  • microsoft_language_tokenizer- This tokenizer divides text based on the grammar of the language you specify.

  • pattern- This tokenizer divides texts where it matches a regular expression that you specify.

  • whitespace- This tokenizer divides text wherever there's white space.

Token Filters

You may want to perform additional processing, such eliminating stopwords or cutting punctuation, once the tokenizer has separated the incoming text into tokens. This processing can be carried out by designating a token filter. There are forty one different token filters available, including:

  • Language-specific filters, such as arabic_normalization. These filters apply language-specific grammar rules to ensure that forms of words are removed and replaced with roots.

  • apostrophe- This filter removes any apostrophe from a token and any characters after the apostrophe.

  • classic- This filter removes English possessives and dots from acronyms.

  • keep- This filter removes any token that doesn't include one or more words from a list you specify.

  • length- This filter removes any token that is longer than your specified minimum or shorter than your specified maximum.

  • trim- This filter removes any leading and trailing white space from tokens.

Conclusion

We have successfully learnt about Tokenizers.

























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