Text Processing Pipeline

Lindera PHP supports a composable text processing pipeline that applies character filters before tokenization and token filters after tokenization. Filters are added to the TokenizerBuilder and executed in the order they are appended.

Input Text
  --> Character Filters (preprocessing)
  --> Tokenization
  --> Token Filters (postprocessing)
  --> Output Tokens

[!NOTE] This page shows a few commonly used filters as examples -- it is not the complete list. lindera-analysis ships 4 character filters and 18 token filters in total. See Filters for the full, authoritative catalogue of every character and token filter, including parameters and examples.

Character Filters

Character filters transform the input text before tokenization.

unicode_normalize

Applies Unicode normalization to the input text.

<?php

$builder = new Lindera\TokenizerBuilder();
$builder->setDictionary('embedded://ipadic');
$builder->appendCharacterFilter('unicode_normalize', ['kind' => 'nfkc']);
$tokenizer = $builder->build();

Supported normalization forms: "nfc", "nfkc", "nfd", "nfkd".

mapping

Replaces characters or strings according to a mapping table.

<?php

$builder = new Lindera\TokenizerBuilder();
$builder->setDictionary('embedded://ipadic');
$builder->appendCharacterFilter('mapping', [
    'mapping' => [
        'リンデラ' => 'lindera',
    ],
]);
$tokenizer = $builder->build();

japanese_iteration_mark

Resolves Japanese iteration marks (odoriji) into their full forms.

<?php

$builder = new Lindera\TokenizerBuilder();
$builder->setDictionary('embedded://ipadic');
$builder->appendCharacterFilter('japanese_iteration_mark', [
    'normalize_kanji' => 'true',
    'normalize_kana' => 'true',
]);
$tokenizer = $builder->build();

Token Filters

Token filters transform or remove tokens after tokenization.

lowercase

Converts token surface forms to lowercase.

<?php

$builder = new Lindera\TokenizerBuilder();
$builder->setDictionary('embedded://ipadic');
$builder->appendTokenFilter('lowercase');
$tokenizer = $builder->build();

japanese_base_form

Replaces inflected forms with their base (dictionary) form using the morphological details from the dictionary.

<?php

$builder = new Lindera\TokenizerBuilder();
$builder->setDictionary('embedded://ipadic');
$builder->appendTokenFilter('japanese_base_form', []);
$tokenizer = $builder->build();

japanese_katakana_stem

Removes the trailing long sound mark from katakana words to normalize spelling variants, stemming only words at least min characters long.

<?php

$builder = new Lindera\TokenizerBuilder();
$builder->setDictionary('embedded://ipadic');
$builder->appendTokenFilter('japanese_katakana_stem', ['min' => 3]);
$tokenizer = $builder->build();

japanese_stop_tags

Removes tokens whose part-of-speech matches any of the specified tags.

<?php

$builder = new Lindera\TokenizerBuilder();
$builder->setDictionary('embedded://ipadic');
$builder->appendTokenFilter('japanese_stop_tags', [
    'tags' => ['助詞', '助動詞'],
]);
$tokenizer = $builder->build();

japanese_keep_tags

Keeps only tokens whose part-of-speech matches one of the specified tags. All other tokens are removed.

<?php

$builder = new Lindera\TokenizerBuilder();
$builder->setDictionary('embedded://ipadic');
$builder->appendTokenFilter('japanese_keep_tags', [
    'tags' => ['名詞'],
]);
$tokenizer = $builder->build();

Complete Pipeline Example

The following example combines multiple character filters and token filters into a single pipeline:

<?php

$builder = new Lindera\TokenizerBuilder();

// Set mode and dictionary
$builder->setMode('normal');
$builder->setDictionary('embedded://ipadic');

// Preprocessing
$builder->appendCharacterFilter('unicode_normalize', ['kind' => 'nfkc']);
$builder->appendCharacterFilter(
    'japanese_iteration_mark',
    ['normalize_kanji' => 'true', 'normalize_kana' => 'true']
);
$builder->appendCharacterFilter('mapping', ['mapping' => ['リンデラ' => 'lindera']]);

// Postprocessing
$builder->appendTokenFilter('japanese_katakana_stem', ['min' => 3]);
$builder->appendTokenFilter('japanese_stop_tags', [
    'tags' => [
        '接続詞',
        '助詞',
        '助詞,格助詞',
        '助詞,格助詞,一般',
        '助詞,係助詞',
        '助詞,副助詞',
        '助詞,終助詞',
        '助詞,連体化',
        '助動詞',
        '記号',
        '記号,一般',
        '記号,読点',
        '記号,句点',
        '記号,空白',
    ],
]);
$builder->appendTokenFilter('lowercase');

// Build the tokenizer
$tokenizer = $builder->build();

// Tokenize
$text = 'Ｌｉｎｄｅｒａは形態素解析ｴﾝｼﾞﾝです。';
$tokens = $tokenizer->tokenize($text);

foreach ($tokens as $token) {
    echo $token->surface . "\t" . implode(',', $token->details) . "\n";
}

In this pipeline:

1. unicode_normalize converts full-width characters to half-width (NFKC normalization)
2. japanese_iteration_mark resolves iteration marks
3. mapping replaces the specified strings
4. japanese_katakana_stem stems katakana words
5. japanese_stop_tags removes particles, auxiliary verbs, and symbols
6. lowercase normalizes alphabetic characters to lowercase