# groonga - An open-source fulltext search engine and column store.

- 日本語

# 7.9.3. `TokenFilterStem`

## 7.9.3.1. Summary

`TokenFilterStem` stems tokenized token.

You need to install an additional package to using `TokenFilterStem`. For more detail of how to installing an additional package, see how to install each OS .

## 7.9.3.2. Syntax

`TokenFilterStem` has optional parameter:

    TokenFilterStem

    TokenFilterStem("algorithm", "steming_algorithm")

## 7.9.3.3. Usage

Here is an example that uses `TokenFilterStem` token filter:

Execution example:

    plugin_register token_filters/stem
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create Memos TABLE_NO_KEY
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Memos content COLUMN_SCALAR ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create Terms TABLE_PAT_KEY ShortText \
      --default_tokenizer TokenBigram \
      --normalizer NormalizerAuto \
      --token_filters TokenFilterStem
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Terms memos_content COLUMN_INDEX|WITH_POSITION Memos content
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    load --table Memos
    [
    {"content": "I develop Groonga"},
    {"content": "I'm developing Groonga"},
    {"content": "I developed Groonga"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 3]
    select Memos --match_columns content --query "develops"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "content",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "I develop Groonga"
    #       ],
    #       [
    #         2,
    #         "I'm developing Groonga"
    #       ],
    #       [
    #         3,
    #         "I developed Groonga"
    #       ]
    #     ]
    #   ]
    # ]

All of `develop`, `developing`, `developed` and `develops` tokens are stemmed as `develop`. So we can find `develop`, `developing` and `developed` by `develops` query.

You can specify steming algorithm except English with `algorithm` option as below.

Execution example:

    plugin_register token_filters/stem
    table_create Memos TABLE_NO_KEY
    column_create Memos content COLUMN_SCALAR ShortText
    table_create Terms TABLE_PAT_KEY ShortText \
      --default_tokenizer TokenBigram \
      --normalizer NormalizerAuto \
      --token_filters 'TokenFilterStem("algorithm", "french")'
    column_create Terms memos_content COLUMN_INDEX|WITH_POSITION Memos content
    load --table Memos
    [
    {"content": "maintenait"},
    {"content": "maintenant"}
    ]
    select Memos --match_columns content --query "maintenir"
    # [
    #   [
    #     0,
    #     0.0,
    #     0.0
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "content",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "maintenait"
    #       ],
    #       [
    #         2,
    #         "maintenant"
    #       ]
    #     ]
    #   ]
    # ]

## 7.9.3.4. Parameters

### 7.9.3.4.1. Optional parameter

There is a optional parameters `algorithm`.

#### 7.9.3.4.1.1. `algorithm`

Specify a steming algorithm.

Steming algorithm is extract the stem. It is prepared for each language.

You can extract the stem of each language by changing steming algorithm. For example, if you want extract the stem of the French, you specify French to `algorithm` option.

Here are support steming algorithm:

    French
    Spanish
    Portuguese
    Italian
    Romanian
    German
    Dutch
    Swedish
    Norwegian
    Danish
    Russian
    Finnish