Text Processing Pipeline

Lindera Node.js 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.

const { TokenizerBuilder } = require("lindera");

const builder = new TokenizerBuilder();
builder.setDictionary("embedded://ipadic");
builder.appendCharacterFilter("unicode_normalize", { kind: "nfkc" });
const tokenizer = builder.build();

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

mapping

Replaces characters or strings according to a mapping table.

const builder = new TokenizerBuilder();
builder.setDictionary("embedded://ipadic");
builder.appendCharacterFilter("mapping", {
  mapping: {
    "\u30fc": "-",
    "\uff5e": "~",
  },
});
const tokenizer = builder.build();

japanese_iteration_mark

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

const builder = new TokenizerBuilder();
builder.setDictionary("embedded://ipadic");
builder.appendCharacterFilter("japanese_iteration_mark", {
  normalize_kanji: true,
  normalize_kana: true,
});
const tokenizer = builder.build();

Token Filters

Token filters transform or remove tokens after tokenization.

lowercase

Converts token surface forms to lowercase.

const builder = new TokenizerBuilder();
builder.setDictionary("embedded://ipadic");
builder.appendTokenFilter("lowercase", {});
const tokenizer = builder.build();

japanese_base_form

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

const builder = new TokenizerBuilder();
builder.setDictionary("embedded://ipadic");
builder.appendTokenFilter("japanese_base_form", {});
const tokenizer = builder.build();

japanese_stop_tags

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

const builder = new TokenizerBuilder();
builder.setDictionary("embedded://ipadic");
builder.appendTokenFilter("japanese_stop_tags", {
  tags: ["助詞,格助詞,一般", "助詞,係助詞", "助詞,連体化", "助動詞"],
});
const tokenizer = builder.build();

[!NOTE] Tags are normalized to exactly four comma-separated levels (missing levels are padded with *) and compared for exact equality against the first four part-of-speech details of each token. A bare 助詞 therefore never matches IPADIC particle tokens — they always carry a subcategory such as 助詞,係助詞 — while a bare 助動詞 does match, because auxiliary verbs have no subcategory (助動詞,*,*,*).

japanese_keep_tags

Keeps only tokens whose part-of-speech matches one of the specified tags. All other tokens are removed. The following keeps only general nouns:

const builder = new TokenizerBuilder();
builder.setDictionary("embedded://ipadic");
builder.appendTokenFilter("japanese_keep_tags", {
  tags: ["名詞,一般"],
});
const tokenizer = builder.build();

Complete Pipeline Example

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

const { TokenizerBuilder } = require("lindera");

const builder = new TokenizerBuilder();
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,
});
// Postprocessing
builder.appendTokenFilter("japanese_base_form", {});
builder.appendTokenFilter("japanese_stop_tags", {
  tags: ["助詞,格助詞,一般", "助詞,係助詞", "助詞,連体化", "助動詞", "記号,句点", "記号,読点"],
});
builder.appendTokenFilter("lowercase", {});
const tokenizer = builder.build();

const tokens = tokenizer.tokenize("Ｌｉｎｄｅｒａは形態素解析を行うライブラリです。");
for (const token of tokens) {
  console.log(`${token.surface}\t${token.details.join(",")}`);
}

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 punctuation
5. lowercase normalizes alphabetic characters to lowercase