7.8.1. Summary

Groonga has tokenizer module that tokenizes text. It is used when the following cases:

  • Indexing text

    ../../_images/used-when-indexing.png

    Tokenizer is used when indexing text.

  • Searching by query

    ../../_images/used-when-searching.png

    Tokenizer is used when searching by query.

Tokenizer is an important module for full-text search. You can change trade-off between precision and recall by changing tokenizer.

Normally, TokenBigram is a suitable tokenizer. If you don’t know much about tokenizer, it’s recommended that you choose TokenBigram.

You can try a tokenizer by tokenize and table_tokenize. Here is an example to try TokenBigram tokenizer by tokenize:

Execution example:

tokenize TokenBigram "Hello World"
# [
#   [
#     0,
#     1337566253.89858,
#     0.000355720520019531
#   ],
#   [
#     {
#       "position": 0,
#       "force_prefix": false,
#       "value": "He"
#     },
#     {
#       "position": 1,
#       "force_prefix": false,
#       "value": "el"
#     },
#     {
#       "position": 2,
#       "force_prefix": false,
#       "value": "ll"
#     },
#     {
#       "position": 3,
#       "force_prefix": false,
#       "value": "lo"
#     },
#     {
#       "position": 4,
#       "force_prefix": false,
#       "value": "o "
#     },
#     {
#       "position": 5,
#       "force_prefix": false,
#       "value": " W"
#     },
#     {
#       "position": 6,
#       "force_prefix": false,
#       "value": "Wo"
#     },
#     {
#       "position": 7,
#       "force_prefix": false,
#       "value": "or"
#     },
#     {
#       "position": 8,
#       "force_prefix": false,
#       "value": "rl"
#     },
#     {
#       "position": 9,
#       "force_prefix": false,
#       "value": "ld"
#     },
#     {
#       "position": 10,
#       "force_prefix": false,
#       "value": "d"
#     }
#   ]
# ]

“tokenize” is the process that extracts zero or more tokens from a text. There are some “tokenize” methods.

For example, Hello World is tokenized to the following tokens by bigram tokenize method:

  • He
  • el
  • ll
  • lo
  • o_ (_ means a white-space)
  • _W (_ means a white-space)
  • Wo
  • or
  • rl
  • ld

In the above example, 10 tokens are extracted from one text Hello World.

For example, Hello World is tokenized to the following tokens by white-space-separate tokenize method:

  • Hello
  • World

In the above example, 2 tokens are extracted from one text Hello World.

Token is used as search key. You can find indexed documents only by tokens that are extracted by used tokenize method. For example, you can find Hello World by ll with bigram tokenize method but you can’t find Hello World by ll with white-space-separate tokenize method. Because white-space-separate tokenize method doesn’t extract ll token. It just extracts Hello and World tokens.

In general, tokenize method that generates small tokens increases recall but decreases precision. Tokenize method that generates large tokens increases precision but decreases recall.

For example, we can find Hello World and A or B by or with bigram tokenize method. Hello World is a noise for people who wants to search “logical and”. It means that precision is decreased. But recall is increased.

We can find only A or B by or with white-space-separate tokenize method. Because World is tokenized to one token World with white-space-separate tokenize method. It means that precision is increased for people who wants to search “logical and”. But recall is decreased because Hello World that contains or isn’t found.