Text Processing Pipeline

Lindera Python 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

Character Filters

Character filters transform the input text before tokenization.

unicode_normalize

Applies Unicode normalization to the input text.

from lindera import TokenizerBuilder

tokenizer = (
    TokenizerBuilder()
    .set_dictionary("embedded://ipadic")
    .append_character_filter("unicode_normalize", {"kind": "nfkc"})
    .build()
)

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

mapping

Replaces characters or strings according to a mapping table.

tokenizer = (
    TokenizerBuilder()
    .set_dictionary("embedded://ipadic")
    .append_character_filter("mapping", {
        "mapping": {
            "\u30fc": "-",
            "\uff5e": "~",
        }
    })
    .build()
)

japanese_iteration_mark

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

tokenizer = (
    TokenizerBuilder()
    .set_dictionary("embedded://ipadic")
    .append_character_filter("japanese_iteration_mark", {
        "normalize_kanji": True,
        "normalize_kana": True,
    })
    .build()
)

Token Filters

Token filters transform or remove tokens after tokenization.

lowercase

Converts token surface forms to lowercase.

tokenizer = (
    TokenizerBuilder()
    .set_dictionary("embedded://ipadic")
    .append_token_filter("lowercase", {})
    .build()
)

japanese_base_form

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

tokenizer = (
    TokenizerBuilder()
    .set_dictionary("embedded://ipadic")
    .append_token_filter("japanese_base_form", {})
    .build()
)

japanese_stop_tags

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

tokenizer = (
    TokenizerBuilder()
    .set_dictionary("embedded://ipadic")
    .append_token_filter("japanese_stop_tags", {
        "tags": ["助詞", "助動詞"],
    })
    .build()
)

japanese_keep_tags

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

tokenizer = (
    TokenizerBuilder()
    .set_dictionary("embedded://ipadic")
    .append_token_filter("japanese_keep_tags", {
        "tags": ["名詞"],
    })
    .build()
)

Complete Pipeline Example

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

from lindera import TokenizerBuilder

tokenizer = (
    TokenizerBuilder()
    .set_mode("normal")
    .set_dictionary("embedded://ipadic")
    # Preprocessing
    .append_character_filter("unicode_normalize", {"kind": "nfkc"})
    .append_character_filter("japanese_iteration_mark", {
        "normalize_kanji": True,
        "normalize_kana": True,
    })
    # Postprocessing
    .append_token_filter("japanese_base_form", {})
    .append_token_filter("japanese_stop_tags", {
        "tags": ["助詞", "助動詞", "記号"],
    })
    .append_token_filter("lowercase", {})
    .build()
)

tokens = tokenizer.tokenize("Ｌｉｎｄｅｒａは形態素解析を行うライブラリです。")
for token in tokens:
    print(f"{token.surface}\t{','.join(token.details)}")

In this pipeline:

1. unicode_normalize converts full-width characters to half-width (NFKC normalization)
2. japanese_iteration_mark resolves iteration marks
3. japanese_base_form converts inflected tokens to base form
4. japanese_stop_tags removes particles, auxiliary verbs, and symbols
5. lowercase normalizes alphabetic characters to lowercase