lindera-python-unidic 2.0.0

Python binding for Lindera with UniDic dictionary

lindera-python

Python binding for Lindera, a Japanese morphological analysis engine.

Overview

lindera-python provides a comprehensive Python interface to the Lindera 1.1.1 morphological analysis engine, supporting Japanese, Korean, and Chinese text analysis. This implementation includes all major features:

• Multi-language Support: Japanese (IPADIC, UniDic), Korean (ko-dic), Chinese (CC-CEDICT)
• Character Filters: Text preprocessing with mapping, regex, Unicode normalization, and Japanese iteration mark handling
• Token Filters: Post-processing filters including lowercase, length filtering, stop words, and Japanese-specific filters
• Flexible Configuration: Configurable tokenization modes and penalty settings
• Metadata Support: Complete dictionary schema and metadata management

Features

Core Components

• TokenizerBuilder: Fluent API for building customized tokenizers
• Tokenizer: High-performance text tokenization with integrated filtering
• CharacterFilter: Pre-processing filters for text normalization
• TokenFilter: Post-processing filters for token refinement
• Metadata & Schema: Dictionary structure and configuration management
• Training & Export (optional): Train custom morphological analysis models from corpus data

Supported Dictionaries

• Japanese: IPADIC (embedded), UniDic (embedded)
• Korean: ko-dic (embedded)
• Chinese: CC-CEDICT (embedded)
• Custom: User dictionary support

Filter Types

Character Filters:

• Mapping filter (character replacement)
• Regex filter (pattern-based replacement)
• Unicode normalization (NFKC, etc.)
• Japanese iteration mark normalization

Token Filters:

• Text case transformation (lowercase, uppercase)
• Length filtering (min/max character length)
• Stop words filtering
• Japanese-specific filters (base form, reading form, etc.)
• Korean-specific filters

Install project dependencies

• pyenv : https://github.com/pyenv/pyenv?tab=readme-ov-file#installation
• Poetry : https://python-poetry.org/docs/#installation
• Rust : https://www.rust-lang.org/tools/install

Install Python

# Install Python
% pyenv install 3.13.5

Setup repository and activate virtual environment

# Clone lindera-python project repository
% git clone [e-mail]:lindera/lindera-python.git
% cd lindera-python

# Set Python version for this project
% pyenv local 3.13.5

# Make Python virtual environment
% python -m venv .venv

# Activate Python virtual environment
% source .venv/bin/activate

# Initialize lindera-python project
(.venv) % make init

Install lindera-python as a library in the virtual environment

This command takes a long time because it builds a library that includes all the dictionaries.

(.venv) % make develop

Quick Start

Basic Tokenization

from lindera import TokenizerBuilder

# Create a tokenizer with default settings
builder = TokenizerBuilder()
builder.set_mode("normal")
builder.set_dictionary("embedded://ipadic")
tokenizer = builder.build()

# Tokenize Japanese text
text = "すもももももももものうち"
tokens = tokenizer.tokenize(text)

for token in tokens:
    print(f"Text: {token.text}, Position: {token.position}")

Using Character Filters

from lindera import TokenizerBuilder

# Create tokenizer builder
builder = TokenizerBuilder()
builder.set_mode("normal")
builder.set_dictionary("embedded://ipadic")

# Add character filters
builder.append_character_filter("mapping", {"mapping": {"ー": "-"}})
builder.append_character_filter("unicode_normalize", {"kind": "nfkc"})

# Build tokenizer with filters
tokenizer = builder.build()
text = "テストー１２３"
tokens = tokenizer.tokenize(text)  # Will apply filters automatically

Using Token Filters

from lindera import TokenizerBuilder

# Create tokenizer builder
builder = TokenizerBuilder()
builder.set_mode("normal")
builder.set_dictionary("embedded://ipadic")

# Add token filters
builder.append_token_filter("lowercase")
builder.append_token_filter("length", {"min": 2, "max": 10})
builder.append_token_filter("japanese_stop_tags", {"tags": ["助詞", "助動詞"]})

# Build tokenizer with filters
tokenizer = builder.build()
tokens = tokenizer.tokenize("テキストの解析")

Integrated Pipeline

from lindera import TokenizerBuilder

# Build tokenizer with integrated filters
builder = TokenizerBuilder()
builder.set_mode("normal")
builder.set_dictionary("embedded://ipadic")

# Add character filters
builder.append_character_filter("mapping", {"mapping": {"ー": "-"}})
builder.append_character_filter("unicode_normalize", {"kind": "nfkc"})

# Add token filters  
builder.append_token_filter("lowercase")
builder.append_token_filter("japanese_base_form")

# Build and use
tokenizer = builder.build()
tokens = tokenizer.tokenize("コーヒーショップ")

Working with Metadata

from lindera import Metadata

# Get metadata for a specific dictionary
metadata = Metadata.load("embedded://ipadic")
print(f"Dictionary: {metadata.dictionary_name}")
print(f"Version: {metadata.dictionary_version}")

# Access schema information
schema = metadata.dictionary_schema
print(f"Schema has {len(schema.fields)} fields")
print(f"Fields: {schema.fields[:5]}")  # First 5 fields

Advanced Usage

Filter Configuration Examples

Character filters and token filters accept configuration as dictionary arguments:

from lindera import TokenizerBuilder

builder = TokenizerBuilder()
builder.set_dictionary("embedded://ipadic")

# Character filters with dict configuration
builder.append_character_filter("unicode_normalize", {"kind": "nfkc"})
builder.append_character_filter("japanese_iteration_mark", {
    "normalize_kanji": "true",
    "normalize_kana": "true"
})
builder.append_character_filter("mapping", {
    "mapping": {"リンデラ": "lindera", "トウキョウ": "東京"}
})

# Token filters with dict configuration  
builder.append_token_filter("japanese_katakana_stem", {"min": 3})
builder.append_token_filter("length", {"min": 2, "max": 10})
builder.append_token_filter("japanese_stop_tags", {
    "tags": ["助詞", "助動詞", "記号"]
})

# Filters without configuration can omit the dict
builder.append_token_filter("lowercase")
builder.append_token_filter("japanese_base_form")

tokenizer = builder.build()

See examples/ directory for comprehensive examples including:

• tokenize.py: Basic tokenization
• tokenize_with_filters.py: Using character and token filters
• tokenize_with_userdict.py: Custom user dictionary
• train_and_export.py: Train and export custom dictionaries (requires train feature)
• Multi-language tokenization
• Advanced configuration options

Dictionary Support

Japanese

• IPADIC: Default Japanese dictionary, good for general text
• UniDic: Academic dictionary with detailed morphological information

Korean

• ko-dic: Standard Korean dictionary for morphological analysis

Chinese

• CC-CEDICT: Community-maintained Chinese-English dictionary

Custom Dictionaries

• User dictionary support for domain-specific terms
• CSV format for easy customization

Dictionary Training (Experimental)

lindera-python supports training custom morphological analysis models from annotated corpus data when built with the train feature.

Building with Training Support

# Install with training support
(.venv) % maturin develop --features train

Training a Model

import lindera

# Train a model from corpus
lindera.train(
    seed="path/to/seed.csv",           # Seed lexicon
    corpus="path/to/corpus.txt",       # Training corpus
    char_def="path/to/char.def",       # Character definitions
    unk_def="path/to/unk.def",         # Unknown word definitions
    feature_def="path/to/feature.def", # Feature templates
    rewrite_def="path/to/rewrite.def", # Rewrite rules
    output="model.dat",                # Output model file
    lambda_=0.01,                      # L1 regularization
    max_iter=100,                      # Max iterations
    max_threads=None                   # Auto-detect CPU cores
)

Exporting Dictionary Files

# Export trained model to dictionary files
lindera.export(
    model="model.dat",              # Trained model
    output="exported_dict/",        # Output directory
    metadata="metadata.json"        # Optional metadata file
)

This will create:

• lex.csv: Lexicon file
• matrix.def: Connection cost matrix
• unk.def: Unknown word definitions
• char.def: Character definitions
• metadata.json: Dictionary metadata (if provided)

See examples/train_and_export.py for a complete example.

API Reference

Core Classes

• TokenizerBuilder: Fluent builder for tokenizer configuration
• Tokenizer: Main tokenization engine
• Token: Individual token with text, position, and linguistic features
• CharacterFilter: Text preprocessing filters
• TokenFilter: Token post-processing filters
• Metadata: Dictionary metadata and configuration
• Schema: Dictionary schema definition

Training Functions (requires train feature)

• train(): Train a morphological analysis model from corpus
• export(): Export trained model to dictionary files

See the test_basic.py file for comprehensive API usage examples.