yurenizer 0.2.1

A library for standardizing terms with spelling variations using a synonym dictionary.

yurenizer

This is a Japanese text normalizer that resolves spelling inconsistencies.

Japanese README is Here.（日本語のREADMEはこちら）
https://github.com/sea-turt1e/yurenizer/blob/main/README_ja.md

Overview

yurenizer is a tool for detecting and unifying variations in Japanese text notation.
For example, it can unify variations like "パソコン" (pasokon), "パーソナル・コンピュータ" (personal computer), and "パーソナルコンピュータ" into "パーソナルコンピューター".
These rules follow the Sudachi Synonym Dictionary.

web-based Demo

You can try the web-based demo here.
yurenizer Web-demo

Installation

pip install yurenizer

Download Synonym Dictionary

curl -L -o synonyms.txt https://raw.githubusercontent.com/WorksApplications/SudachiDict/refs/heads/develop/src/main/text/synonyms.txt

Usage

Quick Start

from yurenizer import SynonymNormalizer, NormalizerConfig
normalizer = SynonymNormalizer(synonym_file_path="synonyms.txt")
text = "「パソコン」は「パーソナルコンピュータ」の「synonym」で、「パーソナル・コンピュータ」と表記することもあります。"
print(normalizer.normalize(text))
# Output: 「パーソナルコンピューター」は「パーソナルコンピューター」の「シノニム」で、「パーソナルコンピューター」と表記することもあります。

Customizing Settings

You can control normalization by specifying NormalizerConfig as an argument to the normalize function.

Example with Custom Settings

from yurenizer import SynonymNormalizer, NormalizerConfig
normalizer = SynonymNormalizer(synonym_file_path="synonyms.txt")
text = "「東日本旅客鉄道」は「JR東」や「JR-East」とも呼ばれます"
config = NormalizerConfig(
            taigen=True, 
            yougen=False,
            expansion="from_another", 
            unify_level="lexeme",
            other_language=False,
            alias=False,
            old_name=False,
            misuse=False,
            alphabetic_abbreviation=True, # Normalize only alphabetic abbreviations
            non_alphabetic_abbreviation=False,
            alphabet=False,
            orthographic_variation=False,
            misspelling=False
        )
print(f"Output: {normalizer.normalize(text, config)}")
# Output: 「東日本旅客鉄道」は「JR東」や「東日本旅客鉄道」とも呼ばれます

---

Configuration Details

The settings in yurenizer are organized hierarchically, allowing you to control the scope and target of normalization.

---

1. taigen / yougen (Target Selection)

Use the taigen and yougen flags to control which parts of speech are included in the normalization.

Setting	Default Value	Description
taigen	True	Includes nouns and other substantives in the normalization. Set to False to exclude them.
yougen	False	Includes verbs and other predicates in the normalization. Set to True to include them (normalized to their lemma).

---

2. expansion (Expansion Flag)

The expansion flag determines how synonyms are expanded based on the synonym dictionary's internal control flags.

Value	Description
from_another	Expands only the synonyms with a control flag value of 0 in the synonym dictionary.
any	Expands all synonyms regardless of their control flag value.

---

3. unify_level (Normalization Level)

Specify the level of normalization with the unify_level parameter.

Value	Description
lexeme	Performs the most comprehensive normalization, targeting all groups (a, b, c) mentioned below.
word_form	Normalizes by word form, targeting groups b and c.
abbreviation	Normalizes by abbreviation, targeting group c only.

---

4. Detailed Normalization Settings (a, b, c Groups)

a Group: Comprehensive Lexical Normalization

Controls normalization based on vocabulary and semantics using the following settings:

Setting	Default Value	Description
other_language	True	Normalizes non-Japanese terms (e.g., English) to Japanese. Set to False to disable this feature.
alias	True	Normalizes aliases. Set to False to disable this feature.
old_name	True	Normalizes old names. Set to False to disable this feature.
misuse	True	Normalizes misused terms. Set to False to disable this feature.

---

b Group: Abbreviation Normalization

Controls normalization of abbreviations using the following settings:

Setting	Default Value	Description
alphabetic_abbreviation	True	Normalizes abbreviations written in alphabetic characters. Set to False to disable this feature.
non_alphabetic_abbreviation	True	Normalizes abbreviations written in non-alphabetic characters (e.g., Japanese). Set to False to disable this feature.

---

c Group: Orthographic Normalization

Controls normalization of orthographic variations and errors using the following settings:

Setting	Default Value	Description
alphabet	True	Normalizes alphabetic variations. Set to False to disable this feature.
orthographic_variation	True	Normalizes orthographic variations. Set to False to disable this feature.
misspelling	True	Normalizes misspellings. Set to False to disable this feature.

---

5. custom_synonym (Custom Dictionary)

If you want to use a custom dictionary, control its behavior with the following setting:

Setting	Default Value	Description
custom_synonym	True	Enables the use of a custom dictionary. Set to False to disable it.

---

This hierarchical configuration allows for flexible normalization by defining the scope and target in detail.

Custom Dictionary Specification

You can specify your own custom dictionary.
If the same word exists in both the custom dictionary and Sudachi synonym dictionary, the custom dictionary takes precedence.

Custom Dictionary Format

The custom dictionary file should be in JSON, CSV, or TSV format.

• JSON file

{
    "Representative word 1": ["Synonym 1_1", "Synonym 1_2", ...],
    "Representative word 2": ["Synonym 2_1", "Synonym 2_2", ...],
}

• CSV file

Representative word 1,Synonym 1_1,Synonym 1_2,...
Representative word 2,Synonym 2_1,Synonym 2_2,...

• TSV file

Representative word 1	Synonym 1_1	Synonym 1_2	...
Representative word 2	Synonym 2_1	Synonym 2_2	...
...

Example

If you create a file like the one below, "幽白", "ゆうはく", and "幽☆遊☆白書" will be normalized to "幽遊白書".

• JSON file

{
    "幽遊白書": ["幽白", "ゆうはく", "幽☆遊☆白書"],
}

• CSV file

幽遊白書,幽白,ゆうはく,幽☆遊☆白書

• TSV file

幽遊白書	幽白	ゆうはく	幽☆遊☆白書

How to Specify

normalizer = SynonymNormalizer(custom_synonyms_file="path/to/custom_dict_file")

Normalization Using a CSV File

You can also normalize text using a CSV file.

Example

JR東日本
JR東
JR-East

Normalize using CsvSynonymNormalizer as shown below.

from yurenizer import CsvSynonymNormalizer
input_file_path = "input.csv"
output_file_path = "output.csv"
csv_normalizer = CsvSynonymNormalizer(synonym_file_path="synonyms.txt")
csv_normalizer.normalize_csv(input_file_path, output_file_path)

The output.csv file will be output as follows.

raw,normalized
JR東日本,東日本旅客鉄道
JR東,東日本旅客鉄道
JR-East,東日本旅客鉄道

Specifying SudachiDict

The length of text segmentation varies depending on the type of SudachiDict. Default is "full", but you can specify "small" or "core".
To use "small" or "core", install it and specify in the SynonymNormalizer() arguments:

pip install sudachidict_small
# or
pip install sudachidict_core

normalizer = SynonymNormalizer(sudachi_dict="small")
# or
normalizer = SynonymNormalizer(sudachi_dict="core")

※ Please refer to SudachiDict documentation for details.

License

This project is licensed under the Apache License 2.0.

Open Source Software Used

• Sudachi Synonym Dictionary: Apache License 2.0
• SudachiPy: Apache License 2.0
• SudachiDict: Apache License 2.0

This library uses SudachiPy and its dictionary SudachiDict for morphological analysis. These are also distributed under the Apache License 2.0.

For detailed license information, please check the LICENSE files of each project:

• Sudachi Synonym Dictionary LICENSE ※ Provided under the same license as the Sudachi dictionary.
• SudachiPy LICENSE
• SudachiDict LICENSE