Lindera CLI

A morphological analysis command-line interface for Lindera.

Install

You can install binary via cargo as follows:

% cargo install lindera-cli

Alternatively, you can download a binary from the following release page:

https://github.com/lindera-morphology/lindera/releases

Build

Build with IPADIC (Japanese dictionary)

The "ipadic" feature flag allows Lindera to include IPADIC.

% cargo build --release --features=embedded-ipadic

Build with UniDic (Japanese dictionary)

The "unidic" feature flag allows Lindera to include UniDic.

% cargo build --release --features=embedded-unidic

Build with ko-dic (Korean dictionary)

The "ko-dic" feature flag allows Lindera to include ko-dic.

% cargo build --release --features=embedded-ko-dic

Build with CC-CEDICT (Chinese dictionary)

The "cc-cedict" feature flag allows Lindera to include CC-CEDICT.

% cargo build --release --features=embedded-cc-cedict

Build without dictionaries

To reduce Lindera's binary size, omit the feature flag. This results in a binary containing only the tokenizer and trainer, as it no longer includes the dictionary.

% cargo build --release

Build with all features

% cargo build --release --all-features

Build dictionary

Build (compile) a morphological analysis dictionary from source CSV files for use with Lindera.

Basic build usage

# Build a system dictionary
lindera build \
  --src /path/to/dictionary/csvs \
  --dest /path/to/output/dictionary \
  --metadata ./lindera-ipadic/metadata.json

# Build a user dictionary
lindera build \
  --src ./user_dict.csv \
  --dest ./user_dictionary \
  --metadata ./lindera-ipadic/metadata.json \
  --user

Build parameters

--src / -s: Source directory containing dictionary CSV files (or single CSV file for user dictionary)
--dest / -d: Destination directory for compiled dictionary output
--metadata / -m: Metadata configuration file (metadata.json) that defines dictionary structure
--user / -u: Build user dictionary instead of system dictionary (optional flag)

Dictionary types

System dictionary

A full morphological analysis dictionary containing:

Lexicon entries (word definitions)
Connection cost matrix
Unknown word handling rules
Character type definitions

User dictionary

A supplementary dictionary for custom words that works alongside a system dictionary.

Examples

Build IPADIC (Japanese dictionary)

# Download and extract IPADIC source files
% curl -L -o /tmp/mecab-ipadic-2.7.0-20250920.tar.gz "https://Lindera.dev/mecab-ipadic-2.7.0-20250920.tar.gz"
% tar zxvf /tmp/mecab-ipadic-2.7.0-20250920.tar.gz -C /tmp

# Build the dictionary
% lindera build \
  --src /tmp/mecab-ipadic-2.7.0-20250920 \
  --dest /tmp/lindera-ipadic-2.7.0-20250920 \
  --metadata ./lindera-ipadic/metadata.json

% ls -al /tmp/lindera-ipadic-2.7.0-20250920
% (cd /tmp && zip -r lindera-ipadic-2.7.0-20250920.zip lindera-ipadic-2.7.0-20250920/)
% tar -czf /tmp/lindera-ipadic-2.7.0-20250920.tar.gz -C /tmp lindera-ipadic-2.7.0-20250920

Build IPADIC NEologd (Japanese dictionary)

# Download and extract IPADIC NEologd source files
% curl -L -o /tmp/mecab-ipadic-neologd-0.0.7-20200820.tar.gz "https://lindera.dev/mecab-ipadic-neologd-0.0.7-20200820.tar.gz"
% tar zxvf /tmp/mecab-ipadic-neologd-0.0.7-20200820.tar.gz -C /tmp

# Build the dictionary
% lindera build \
  --src /tmp/mecab-ipadic-neologd-0.0.7-20200820 \
  --dest /tmp/lindera-ipadic-neologd-0.0.7-20200820 \
  --metadata ./lindera-ipadic-neologd/metadata.json

% ls -al /tmp/lindera-ipadic-neologd-0.0.7-20200820
% (cd /tmp && zip -r lindera-ipadic-neologd-0.0.7-20200820.zip lindera-ipadic-neologd-0.0.7-20200820/)
% tar -czf /tmp/lindera-ipadic-neologd-0.0.7-20200820.tar.gz -C /tmp lindera-ipadic-neologd-0.0.7-20200820

Build UniDic (Japanese dictionary)

# Download and extract UniDic source files
% curl -L -o /tmp/unidic-mecab-2.1.2.tar.gz "https://Lindera.dev/unidic-mecab-2.1.2.tar.gz"
% tar zxvf /tmp/unidic-mecab-2.1.2.tar.gz -C /tmp

# Build the dictionary
% lindera build \
  --src /tmp/unidic-mecab-2.1.2 \
  --dest /tmp/lindera-unidic-2.1.2 \
  --metadata ./lindera-unidic/metadata.json

% ls -al /tmp/lindera-unidic-2.1.2
% (cd /tmp && zip -r lindera-unidic-2.1.2.zip lindera-unidic-2.1.2/)
% tar -czf /tmp/lindera-unidic-2.1.2.tar.gz -C /tmp lindera-unidic-2.1.2

Build CC-CEDICT (Chinese dictionary)

# Download and extract CC-CEDICT source files
% curl -L -o /tmp/CC-CEDICT-MeCab-0.1.0-20200409.tar.gz "https://lindera.dev/CC-CEDICT-MeCab-0.1.0-20200409.tar.gz"
% tar zxvf /tmp/CC-CEDICT-MeCab-0.1.0-20200409.tar.gz -C /tmp

# Build the dictionary
% lindera build \
  --src /tmp/CC-CEDICT-MeCab-0.1.0-20200409 \
  --dest /tmp/lindera-cc-cedict-0.1.0-20200409 \
  --metadata ./lindera-cc-cedict/metadata.json

% ls -al /tmp/lindera-cc-cedict-0.1.0-20200409
% (cd /tmp && zip -r lindera-cc-cedict-0.1.0-20200409.zip lindera-cc-cedict-0.1.0-20200409/)
% tar -czf /tmp/lindera-cc-cedict-0.1.0-20200409.tar.gz -C /tmp lindera-cc-cedict-0.1.0-20200409

Build ko-dic (Korean dictionary)

# Download and extract ko-dic source files
% curl -L -o /tmp/mecab-ko-dic-2.1.1-20180720.tar.gz "https://Lindera.dev/mecab-ko-dic-2.1.1-20180720.tar.gz"
% tar zxvf /tmp/mecab-ko-dic-2.1.1-20180720.tar.gz -C /tmp

# Build the dictionary
% lindera build \
  --src /tmp/mecab-ko-dic-2.1.1-20180720 \
  --dest /tmp/lindera-ko-dic-2.1.1-20180720 \
  --metadata ./lindera-ko-dic/metadata.json

% ls -al /tmp/lindera-ko-dic-2.1.1-20180720
% (cd /tmp && zip -r lindera-ko-dic-2.1.1-20180720.zip lindera-ko-dic-2.1.1-20180720/)
% tar -czf /tmp/lindera-ko-dic-2.1.1-20180720.tar.gz -C /tmp lindera-ko-dic-2.1.1-20180720

Build user dictionary

Build IPADIC user dictionary (Japanese)

For more details about user dictionary format please refer to the following URL:

Lindera IPADIC Builder/User Dictionary Format

% lindera build \
  --src ./resources/ipadic_simple_userdic.csv \
  --dest ./resources \
  --metadata ./lindera-ipadic/metadata.json \
  --user

Build UniDic user dictionary (Japanese)

For more details about user dictionary format please refer to the following URL:

Lindera UniDic Builder/User Dictionary Format

% lindera build \
  --src ./resources/unidic_simple_userdic.csv \
  --dest ./resources \
  --metadata ./lindera-unidic/metadata.json \
  --user

Build CC-CEDICT user dictionary (Chinese)

For more details about user dictionary format please refer to the following URL:

Lindera CC-CEDICT Builder/User Dictionary Format

% lindera build \
  --src ./resources/cc-cedict_simple_userdic.csv \
  --dest ./resources \
  --metadata ./lindera-cc-cedict/metadata.json \
  --user

Build ko-dic user dictionary (Korean)

For more details about user dictionary format please refer to the following URL:

Lindera ko-dic Builder/User Dictionary Format

% lindera build \
  --src ./resources/ko-dic_simple_userdic.csv \
  --dest ./resources \
  --metadata ./lindera-ko-dic/metadata.json \
  --user

Tokenize text

Perform morphological analysis (tokenization) on Japanese, Chinese, or Korean text using various dictionaries.

Basic tokenization usage

# Tokenize text using a dictionary directory
echo "日本語の形態素解析を行うことができます。" | lindera tokenize \
  --dict /path/to/dictionary

# Tokenize text using embedded dictionary
echo "日本語の形態素解析を行うことができます。" | lindera tokenize \
  --dict embedded://ipadic

# Tokenize with different output format
echo "日本語の形態素解析を行うことができます。" | lindera tokenize \
  --dict embedded://ipadic \
  --output json

# Tokenize text from file
lindera tokenize \
  --dict /path/to/dictionary \
  --output wakati \
  input.txt

Tokenization parameters

--dict / -d: Dictionary path or URI (required)
- File path: /path/to/dictionary
- Embedded: embedded://ipadic, embedded://unidic, etc.
--output / -o: Output format (default: mecab)
- mecab: MeCab-compatible format with part-of-speech info
- wakati: Space-separated tokens only
- json: Detailed JSON format with all token information
--user-dict / -u: User dictionary path (optional)
--mode / -m: Tokenization mode (default: normal)
- normal: Standard tokenization
- decompose: Decompose compound words
--char-filter / -c: Character filter configuration (JSON)
--token-filter / -t: Token filter configuration (JSON)
Input file: Optional file path (default: stdin)

Examples with external dictionaries

Tokenize with external IPADIC (Japanese dictionary)

% echo "日本語の形態素解析を行うことができます。" | lindera tokenize \
  --dict /tmp/lindera-ipadic-2.7.0-20250920

日本語  名詞,一般,*,*,*,*,日本語,ニホンゴ,ニホンゴ
の      助詞,連体化,*,*,*,*,の,ノ,ノ
形態素  名詞,一般,*,*,*,*,形態素,ケイタイソ,ケイタイソ
解析    名詞,サ変接続,*,*,*,*,解析,カイセキ,カイセキ
を      助詞,格助詞,一般,*,*,*,を,ヲ,ヲ
行う    動詞,自立,*,*,五段・ワ行促音便,基本形,行う,オコナウ,オコナウ
こと    名詞,非自立,一般,*,*,*,こと,コト,コト
が      助詞,格助詞,一般,*,*,*,が,ガ,ガ
でき    動詞,自立,*,*,一段,連用形,できる,デキ,デキ
ます    助動詞,*,*,*,特殊・マス,基本形,ます,マス,マス
。      記号,句点,*,*,*,*,。,。,。
EOS

Tokenize with external IPADIC Neologd (Japanese dictionary)

% echo "日本語の形態素解析を行うことができます。" | lindera tokenize \
  --dict /tmp/lindera-ipadic-neologd-0.0.7-20200820

日本語  名詞,一般,*,*,*,*,日本語,ニホンゴ,ニホンゴ
の      助詞,連体化,*,*,*,*,の,ノ,ノ
形態素解析      名詞,固有名詞,一般,*,*,*,形態素解析,ケイタイソカイセキ,ケイタイソカイセキ
を      助詞,格助詞,一般,*,*,*,を,ヲ,ヲ
行う    動詞,自立,*,*,五段・ワ行促音便,基本形,行う,オコナウ,オコナウ
こと    名詞,非自立,一般,*,*,*,こと,コト,コト
が      助詞,格助詞,一般,*,*,*,が,ガ,ガ
でき    動詞,自立,*,*,一段,連用形,できる,デキ,デキ
ます    助動詞,*,*,*,特殊・マス,基本形,ます,マス,マス
。      記号,句点,*,*,*,*,。,。,。
EOS

Tokenize with external UniDic (Japanese dictionary)

% echo "日本語の形態素解析を行うことができます。" | lindera tokenize \
  --dict /tmp/lindera-unidic-2.1.2

日本    名詞,固有名詞,地名,国,*,*,ニッポン,日本,日本,ニッポン,日本,ニッポン,固,*,*,*,*
語      名詞,普通名詞,一般,*,*,*,ゴ,語,語,ゴ,語,ゴ,漢,*,*,*,*
の      助詞,格助詞,*,*,*,*,ノ,の,の,ノ,の,ノ,和,*,*,*,*
形態    名詞,普通名詞,一般,*,*,*,ケイタイ,形態,形態,ケータイ,形態,ケータイ,漢,*,*,*,*
素      接尾辞,名詞的,一般,*,*,*,ソ,素,素,ソ,素,ソ,漢,*,*,*,*
解析    名詞,普通名詞,サ変可能,*,*,*,カイセキ,解析,解析,カイセキ,解析,カイセキ,漢,*,*,*,*
を      助詞,格助詞,*,*,*,*,ヲ,を,を,オ,を,オ,和,*,*,*,*
行う    動詞,一般,*,*,五段-ワア行,連体形-一般,オコナウ,行う,行う,オコナウ,行う,オコナウ,和,*,*,*,*
こと    名詞,普通名詞,一般,*,*,*,コト,事,こと,コト,こと,コト,和,コ濁,基本形,*,*
が      助詞,格助詞,*,*,*,*,ガ,が,が,ガ,が,ガ,和,*,*,*,*
でき    動詞,非自立可能,*,*,上一段-カ行,連用形-一般,デキル,出来る,でき,デキ,できる,デキル,和,*,*,*,*
ます    助動詞,*,*,*,助動詞-マス,終止形-一般,マス,ます,ます,マス,ます,マス,和,*,*,*,*
。      補助記号,句点,*,*,*,*,,。,。,,。,,記号,*,*,*,*
EOS

Tokenize with external ko-dic (Korean dictionary)

% echo "한국어의형태해석을실시할수있습니다." | lindera tokenize \
  --dict /tmp/lindera-ko-dic-2.1.1-20180720

한국어  NNG,*,F,한국어,Compound,*,*,한국/NNG/*+어/NNG/*
의      JKG,*,F,의,*,*,*,*
형태    NNG,*,F,형태,*,*,*,*
해석    NNG,행위,T,해석,*,*,*,*
을      JKO,*,T,을,*,*,*,*
실시    NNG,행위,F,실시,*,*,*,*
할      VV+ETM,*,T,할,Inflect,VV,ETM,하/VV/*+ᆯ/ETM/*
수      NNG,*,F,수,*,*,*,*
있      VX,*,T,있,*,*,*,*
습니다  EF,*,F,습니다,*,*,*,*
.       UNK
EOS

Tokenize with external CC-CEDICT (Chinese dictionary)

% echo "可以进行中文形态学分析。" | lindera tokenize \
  --dict /tmp/lindera-cc-cedict-0.1.0-20200409

可以    *,*,*,*,ke3 yi3,可以,可以,can/may/possible/able to/not bad/pretty good/
进行    *,*,*,*,jin4 xing2,進行,进行,to advance/to conduct/underway/in progress/to do/to carry out/to carry on/to execute/
中文    *,*,*,*,Zhong1 wen2,中文,中文,Chinese language/
形态学  *,*,*,*,xing2 tai4 xue2,形態學,形态学,morphology (in biology or linguistics)/
分析    *,*,*,*,fen1 xi1,分析,分析,to analyze/analysis/CL:個|个[ge4]/
。      UNK
EOS

Examples with embedded dictionaries

Lindera can include dictionaries directly in the binary when built with specific feature flags. This allows tokenization without external dictionary files.

Tokenize with embedded IPADIC (Japanese dictionary)

% echo "日本語の形態素解析を行うことができます。" | lindera tokenize \
  --dict embedded://ipadic

日本語  名詞,一般,*,*,*,*,日本語,ニホンゴ,ニホンゴ
の      助詞,連体化,*,*,*,*,の,ノ,ノ
形態素  名詞,一般,*,*,*,*,形態素,ケイタイソ,ケイタイソ
解析    名詞,サ変接続,*,*,*,*,解析,カイセキ,カイセキ
を      助詞,格助詞,一般,*,*,*,を,ヲ,ヲ
行う    動詞,自立,*,*,五段・ワ行促音便,基本形,行う,オコナウ,オコナウ
こと    名詞,非自立,一般,*,*,*,こと,コト,コト
が      助詞,格助詞,一般,*,*,*,が,ガ,ガ
でき    動詞,自立,*,*,一段,連用形,できる,デキ,デキ
ます    助動詞,*,*,*,特殊・マス,基本形,ます,マス,マス
。      記号,句点,*,*,*,*,。,。,。
EOS

NOTE: To include IPADIC dictionary in the binary, you must build with the --features=embedded-ipadic option.

Tokenize with embedded UniDic (Japanese dictionary)

% echo "日本語の形態素解析を行うことができます。" | lindera tokenize \
  --dict embedded://unidic

日本    名詞,固有名詞,地名,国,*,*,ニッポン,日本,日本,ニッポン,日本,ニッポン,固,*,*,*,*
語      名詞,普通名詞,一般,*,*,*,ゴ,語,語,ゴ,語,ゴ,漢,*,*,*,*
の      助詞,格助詞,*,*,*,*,ノ,の,の,ノ,の,ノ,和,*,*,*,*
形態    名詞,普通名詞,一般,*,*,*,ケイタイ,形態,形態,ケータイ,形態,ケータイ,漢,*,*,*,*
素      接尾辞,名詞的,一般,*,*,*,ソ,素,素,ソ,素,ソ,漢,*,*,*,*
解析    名詞,普通名詞,サ変可能,*,*,*,カイセキ,解析,解析,カイセキ,解析,カイセキ,漢,*,*,*,*
を      助詞,格助詞,*,*,*,*,ヲ,を,を,オ,を,オ,和,*,*,*,*
行う    動詞,一般,*,*,五段-ワア行,連体形-一般,オコナウ,行う,行う,オコナウ,行う,オコナウ,和,*,*,*,*
こと    名詞,普通名詞,一般,*,*,*,コト,事,こと,コト,こと,コト,和,コ濁,基本形,*,*
が      助詞,格助詞,*,*,*,*,ガ,が,が,ガ,が,ガ,和,*,*,*,*
でき    動詞,非自立可能,*,*,上一段-カ行,連用形-一般,デキル,出来る,でき,デキ,できる,デキル,和,*,*,*,*
ます    助動詞,*,*,*,助動詞-マス,終止形-一般,マス,ます,ます,マス,ます,マス,和,*,*,*,*
。      補助記号,句点,*,*,*,*,,。,。,,。,,記号,*,*,*,*
EOS

NOTE: To include UniDic dictionary in the binary, you must build with the --features=embedded-unidic option.

Tokenize with embedded IPADIC NEologd (Japanese dictionary)

% echo "日本語の形態素解析を行うことができます。" | lindera tokenize \
  --dict embedded://ipadic-neologd

日本語  名詞,一般,*,*,*,*,日本語,ニホンゴ,ニホンゴ
の      助詞,連体化,*,*,*,*,の,ノ,ノ
形態素解析      名詞,固有名詞,一般,*,*,*,形態素解析,ケイタイソカイセキ,ケイタイソカイセキ
を      助詞,格助詞,一般,*,*,*,を,ヲ,ヲ
行う    動詞,自立,*,*,五段・ワ行促音便,基本形,行う,オコナウ,オコナウ
こと    名詞,非自立,一般,*,*,*,こと,コト,コト
が      助詞,格助詞,一般,*,*,*,が,ガ,ガ
でき    動詞,自立,*,*,一段,連用形,できる,デキ,デキ
ます    助動詞,*,*,*,特殊・マス,基本形,ます,マス,マス
。      記号,句点,*,*,*,*,。,。,。
EOS

NOTE: To include UniDic dictionary in the binary, you must build with the --features=embedded-ipadic-neologd option.

Tokenize with embedded ko-dic (Korean dictionary)

% echo "한국어의형태해석을실시할수있습니다." | lindera tokenize \
  --dict embedded://ko-dic

한국어  NNG,*,F,한국어,Compound,*,*,한국/NNG/*+어/NNG/*
의      JKG,*,F,의,*,*,*,*
형태    NNG,*,F,형태,*,*,*,*
해석    NNG,행위,T,해석,*,*,*,*
을      JKO,*,T,을,*,*,*,*
실시    NNG,행위,F,실시,*,*,*,*
할      VV+ETM,*,T,할,Inflect,VV,ETM,하/VV/*+ᆯ/ETM/*
수      NNG,*,F,수,*,*,*,*
있      VX,*,T,있,*,*,*,*
습니다  EF,*,F,습니다,*,*,*,*
.       UNK
EOS

NOTE: To include ko-dic dictionary in the binary, you must build with the --features=embedded-ko-dic option.

Tokenize with embedded CC-CEDICT (Chinese dictionary)

% echo "可以进行中文形态学分析。" | lindera tokenize \
  --dict embedded://cc-cedict

可以    *,*,*,*,ke3 yi3,可以,可以,can/may/possible/able to/not bad/pretty good/
进行    *,*,*,*,jin4 xing2,進行,进行,to advance/to conduct/underway/in progress/to do/to carry out/to carry on/to execute/
中文    *,*,*,*,Zhong1 wen2,中文,中文,Chinese language/
形态学  *,*,*,*,xing2 tai4 xue2,形態學,形态学,morphology (in biology or linguistics)/
分析    *,*,*,*,fen1 xi1,分析,分析,to analyze/analysis/CL:個|个[ge4]/
。      UNK
EOS

NOTE: To include CC-CEDICT dictionary in the binary, you must build with the --features=embedded-cc-cedict option.

User dictionary examples

Lindera supports user dictionaries to add custom words alongside system dictionaries. User dictionaries can be in CSV or binary format.

Use user dictionary (CSV format)

% echo "東京スカイツリーの最寄り駅はとうきょうスカイツリー駅です" | lindera tokenize \
  --dict embedded://ipadic \
  --user-dict ./resources/ipadic_simple_userdic.csv

東京スカイツリー        カスタム名詞,*,*,*,*,*,東京スカイツリー,トウキョウスカイツリー,*
の      助詞,連体化,*,*,*,*,の,ノ,ノ
最寄り駅        名詞,一般,*,*,*,*,最寄り駅,モヨリエキ,モヨリエキ
は      助詞,係助詞,*,*,*,*,は,ハ,ワ
とうきょうスカイツリー駅        カスタム名詞,*,*,*,*,*,とうきょうスカイツリー駅,トウキョウスカイツリーエキ,*
です    助動詞,*,*,*,特殊・デス,基本形,です,デス,デス
EOS

Use user dictionary (Binary format)

% echo "東京スカイツリーの最寄り駅はとうきょうスカイツリー駅です" | lindera tokenize \
  --dict /tmp/lindera-ipadic-2.7.0-20250920 \
  --user-dict ./resources/ipadic_simple_userdic.bin

東京スカイツリー        カスタム名詞,*,*,*,*,*,東京スカイツリー,トウキョウスカイツリー,*
の      助詞,連体化,*,*,*,*,の,ノ,ノ
最寄り駅        名詞,一般,*,*,*,*,最寄り駅,モヨリエキ,モヨリエキ
は      助詞,係助詞,*,*,*,*,は,ハ,ワ
とうきょうスカイツリー駅        カスタム名詞,*,*,*,*,*,とうきょうスカイツリー駅,トウキョウスカイツリーエキ,*
です    助動詞,*,*,*,特殊・デス,基本形,です,デス,デス
EOS

Tokenization modes

Lindera provides two tokenization modes: normal and decompose.

Normal mode (default)

Tokenizes faithfully based on words registered in the dictionary:

% echo "関西国際空港限定トートバッグ" | lindera tokenize \
  --dict embedded://ipadic \
  --mode normal

関西国際空港    名詞,固有名詞,組織,*,*,*,関西国際空港,カンサイコクサイクウコウ,カンサイコクサイクーコー
限定    名詞,サ変接続,*,*,*,*,限定,ゲンテイ,ゲンテイ
トートバッグ    UNK,*,*,*,*,*,*,*,*
EOS

Decompose mode

Tokenizes compound noun words additionally:

% echo "関西国際空港限定トートバッグ" | lindera tokenize \
  --dict embedded://ipadic \
  --mode decompose

関西    名詞,固有名詞,地域,一般,*,*,関西,カンサイ,カンサイ
国際    名詞,一般,*,*,*,*,国際,コクサイ,コクサイ
空港    名詞,一般,*,*,*,*,空港,クウコウ,クーコー
限定    名詞,サ変接続,*,*,*,*,限定,ゲンテイ,ゲンテイ
トートバッグ    UNK,*,*,*,*,*,*,*,*
EOS

Output formats

Lindera provides three output formats: mecab, wakati and json.

MeCab format (default)

Outputs results in MeCab-compatible format with part-of-speech information:

% echo "お待ちしております。" | lindera tokenize \
  --dict embedded://ipadic \
  --output mecab

お待ち  名詞,サ変接続,*,*,*,*,お待ち,オマチ,オマチ
し  動詞,自立,*,*,サ変・スル,連用形,する,シ,シ
て  助詞,接続助詞,*,*,*,*,て,テ,テ
おり  動詞,非自立,*,*,五段・ラ行,連用形,おる,オリ,オリ
ます  助動詞,*,*,*,特殊・マス,基本形,ます,マス,マス
。  記号,句点,*,*,*,*,。,。,。
EOS

Wakati format

Outputs only the token text separated by spaces:

% echo "お待ちしております。" | lindera tokenize \
  --dict embedded://ipadic \
  --output wakati

お待ち し て おり ます 。

JSON format

Outputs detailed token information in JSON format:

% echo "お待ちしております。" | lindera tokenize \
  --dict embedded://ipadic \
  --output json

[
  {
    "base_form": "お待ち",
    "byte_end": 9,
    "byte_start": 0,
    "conjugation_form": "*",
    "conjugation_type": "*",
    "part_of_speech": "名詞",
    "part_of_speech_subcategory_1": "サ変接続",
    "part_of_speech_subcategory_2": "*",
    "part_of_speech_subcategory_3": "*",
    "pronunciation": "オマチ",
    "reading": "オマチ",
    "surface": "お待ち",
    "word_id": 14698
  },
  {
    "base_form": "する",
    "byte_end": 12,
    "byte_start": 9,
    "conjugation_form": "サ変・スル",
    "conjugation_type": "連用形",
    "part_of_speech": "動詞",
    "part_of_speech_subcategory_1": "自立",
    "part_of_speech_subcategory_2": "*",
    "part_of_speech_subcategory_3": "*",
    "pronunciation": "シ",
    "reading": "シ",
    "surface": "し",
    "word_id": 30763
  },
  {
    "base_form": "て",
    "byte_end": 15,
    "byte_start": 12,
    "conjugation_form": "*",
    "conjugation_type": "*",
    "part_of_speech": "助詞",
    "part_of_speech_subcategory_1": "接続助詞",
    "part_of_speech_subcategory_2": "*",
    "part_of_speech_subcategory_3": "*",
    "pronunciation": "テ",
    "reading": "テ",
    "surface": "て",
    "word_id": 46603
  },
  {
    "base_form": "おる",
    "byte_end": 21,
    "byte_start": 15,
    "conjugation_form": "五段・ラ行",
    "conjugation_type": "連用形",
    "part_of_speech": "動詞",
    "part_of_speech_subcategory_1": "非自立",
    "part_of_speech_subcategory_2": "*",
    "part_of_speech_subcategory_3": "*",
    "pronunciation": "オリ",
    "reading": "オリ",
    "surface": "おり",
    "word_id": 14239
  },
  {
    "base_form": "ます",
    "byte_end": 27,
    "byte_start": 21,
    "conjugation_form": "特殊・マス",
    "conjugation_type": "基本形",
    "part_of_speech": "助動詞",
    "part_of_speech_subcategory_1": "*",
    "part_of_speech_subcategory_2": "*",
    "part_of_speech_subcategory_3": "*",
    "pronunciation": "マス",
    "reading": "マス",
    "surface": "ます",
    "word_id": 68733
  },
  {
    "base_form": "。",
    "byte_end": 30,
    "byte_start": 27,
    "conjugation_form": "*",
    "conjugation_type": "*",
    "part_of_speech": "記号",
    "part_of_speech_subcategory_1": "句点",
    "part_of_speech_subcategory_2": "*",
    "part_of_speech_subcategory_3": "*",
    "pronunciation": "。",
    "reading": "。",
    "surface": "。",
    "word_id": 101
  }
]

Advanced tokenization

Lindera provides an analytical framework that combines character filters, tokenizers, and token filters for advanced text processing. Filters are configured using JSON.

Tokenize with character and token filters

% echo "すもももももももものうち" | lindera tokenize \
  --dict embedded://ipadic \
  --char-filter 'unicode_normalize:{"kind":"nfkc"}' \
  --token-filter 'japanese_keep_tags:{"tags":["名詞,一般"]}'

すもも  名詞,一般,*,*,*,*,すもも,スモモ,スモモ
もも    名詞,一般,*,*,*,*,もも,モモ,モモ
もも    名詞,一般,*,*,*,*,もも,モモ,モモ
EOS

Dictionary Training (Experimental)

Train a new morphological analysis model from annotated corpus data. To use this feature, you must build with the train feature flag enabled. (The train feature flag is enabled by default.)

Training parameters

--seed / -s: Seed lexicon file (CSV format) to be weighted
--corpus / -c: Training corpus (annotated text)
--char-def / -C: Character definition file (char.def)
--unk-def / -u: Unknown word definition file (unk.def) to be weighted
--feature-def / -f: Feature definition file (feature.def)
--rewrite-def / -r: Rewrite rule definition file (rewrite.def)
--output / -o: Output model file
--lambda / -l: L1 regularization (0.0-1.0) (default: 0.01)
--max-iterations / -i: Maximum number of iterations for training (default: 100)
--max-threads / -t: Maximum number of threads (defaults to CPU core count, auto-adjusted based on dataset size)

Basic workflow

1. Prepare training files

Seed lexicon file (seed.csv):

The seed lexicon file contains initial dictionary entries used for training the CRF model. Each line represents a word entry with comma-separated fields. The specific field structure varies depending on the dictionary format:

Surface
Left context ID
Right context ID
Word cost
Part-of-speech tags (multiple fields)
Base form
Reading (katakana)
Pronunciation

Note: The exact field definitions differ between dictionary formats (IPADIC, UniDic, ko-dic, CC-CEDICT). Please refer to each dictionary's format specification for details.

外国,0,0,0,名詞,一般,*,*,*,*,外国,ガイコク,ガイコク
人,0,0,0,名詞,接尾,一般,*,*,*,人,ジン,ジン

Training corpus (corpus.txt):

The training corpus file contains annotated text data used to train the CRF model. Each line consists of:

A surface form (word) followed by a tab character
Comma-separated morphological features (part-of-speech tags, base form, reading, pronunciation)
Sentences are separated by "EOS" (End Of Sentence) markers

Note: The morphological feature format varies depending on the dictionary (IPADIC, UniDic, ko-dic, CC-CEDICT). Please refer to each dictionary's format specification for details.

外国	名詞,一般,*,*,*,*,外国,ガイコク,ガイコク
人	名詞,接尾,一般,*,*,*,人,ジン,ジン
参政	名詞,サ変接続,*,*,*,*,参政,サンセイ,サンセイ
権	名詞,接尾,一般,*,*,*,権,ケン,ケン
EOS

これ	連体詞,*,*,*,*,*,これ,コレ,コレ
は	助詞,係助詞,*,*,*,*,は,ハ,ワ
テスト	名詞,サ変接続,*,*,*,*,テスト,テスト,テスト
です	助動詞,*,*,*,特殊・デス,基本形,です,デス,デス
。	記号,句点,*,*,*,*,。,。,。
EOS

形態	名詞,一般,*,*,*,*,形態,ケイタイ,ケイタイ
素	名詞,接尾,一般,*,*,*,素,ソ,ソ
解析	名詞,サ変接続,*,*,*,*,解析,カイセキ,カイセキ
を	助詞,格助詞,一般,*,*,*,を,ヲ,ヲ
行う	動詞,自立,*,*,五段・ワ行促音便,基本形,行う,オコナウ,オコナウ
EOS

For detailed information about file formats and advanced features, see TRAINER_README.md.

2. Train model

lindera train \
  --seed ./resources/training/seed.csv \
  --corpus ./resources/training/corpus.txt \
  --unk-def ./resources/training/unk.def \
  --char-def ./resources/training/char.def \
  --feature-def ./resources/training/feature.def \
  --rewrite-def ./resources/training/rewrite.def \
  --output /tmp/lindera/training/model.dat \
  --lambda 0.01 \
  --max-iterations 100

3. Training results

The trained model will contain:

Existing words: All seed dictionary records with newly learned weights
New words: Words from the corpus not in the seed dictionary, added with appropriate weights

Export trained model to dictionary

Export a trained model file to Lindera dictionary format files. This feature requires building with the train feature flag enabled.

Basic export usage

# Export trained model to dictionary files
lindera export \
  --model /tmp/lindera/training/model.dat \
  --metadata ./resources/training/metadata.json \
  --output /tmp/lindera/training/dictionary

Export parameters

--model / -m: Path to the trained model file (.dat format)
--output / -o: Directory to output the dictionary files
--metadata: Optional metadata.json file to update with trained model information

Output files

The export command creates the following dictionary files in the output directory:

lex.csv: Lexicon file with learned weights
matrix.def: Connection cost matrix
unk.def: Unknown word definitions
char.def: Character type definitions
metadata.json: Updated metadata file (if --metadata option is provided)

Complete workflow example

1. Train model

lindera train \
  --seed ./resources/training/seed.csv \
  --corpus ./resources/training/corpus.txt \
  --unk-def ./resources/training/unk.def \
  --char-def ./resources/training/char.def \
  --feature-def ./resources/training/feature.def \
  --rewrite-def ./resources/training/rewrite.def \
  --output /tmp/lindera/training/model.dat \
  --lambda 0.01 \
  --max-iterations 100

2. Export to dictionary format

lindera export \
  --model /tmp/lindera/training/model.dat \
  --metadata ./resources/training/metadata.json \
  --output /tmp/lindera/training/dictionary

3. Build dictionary

lindera build \
  --src /tmp/lindera/training/dictionary \
  --dest /tmp/lindera/training/compiled_dictionary \
  --metadata /tmp/lindera/training/dictionary/metadata.json

4. Use trained dictionary

echo "これは外国人参政権です。" | lindera tokenize \
  -d /tmp/lindera/training/compiled_dictionary

Metadata update feature

When the --metadata option is provided, the export command will:

Read the base metadata.json file to preserve existing configuration
Update specific fields with values from the trained model:
- default_left_context_id: Maximum left context ID from trained model
- default_right_context_id: Maximum right context ID from trained model
- default_word_cost: Calculated from feature weight median
- model_info: Training statistics including:
  - feature_count: Number of features in the model
  - label_count: Number of labels in the model
  - max_left_context_id: Maximum left context ID
  - max_right_context_id: Maximum right context ID
  - connection_matrix_size: Size of connection cost matrix
  - training_iterations: Number of training iterations performed
  - regularization: L1 regularization parameter used
  - version: Model version
  - updated_at: Timestamp of when the model was exported
Preserve existing settings such as:
- Dictionary name
- Character encoding settings
- Schema definitions
- Other user-defined configuration

This allows you to maintain your base dictionary configuration while incorporating the optimized parameters learned during training.

API reference

The API reference is available. Please see following URL:

lindera-cli

lindera-cli 1.4.1

Lindera CLI

Install

Build

Build with IPADIC (Japanese dictionary)

Build with UniDic (Japanese dictionary)

Build with ko-dic (Korean dictionary)

Build with CC-CEDICT (Chinese dictionary)

Build without dictionaries

Build with all features

Build dictionary

Basic build usage

Build parameters

Dictionary types

System dictionary

User dictionary

Examples

Build IPADIC (Japanese dictionary)

Build IPADIC NEologd (Japanese dictionary)

Build UniDic (Japanese dictionary)

Build CC-CEDICT (Chinese dictionary)

Build ko-dic (Korean dictionary)

Build user dictionary

Build IPADIC user dictionary (Japanese)

Build UniDic user dictionary (Japanese)

Build CC-CEDICT user dictionary (Chinese)

Build ko-dic user dictionary (Korean)

Tokenize text

Basic tokenization usage

Tokenization parameters

Examples with external dictionaries

Tokenize with external IPADIC (Japanese dictionary)

Tokenize with external IPADIC Neologd (Japanese dictionary)

Tokenize with external UniDic (Japanese dictionary)

Tokenize with external ko-dic (Korean dictionary)

Tokenize with external CC-CEDICT (Chinese dictionary)

Examples with embedded dictionaries

Tokenize with embedded IPADIC (Japanese dictionary)

Tokenize with embedded UniDic (Japanese dictionary)

Tokenize with embedded IPADIC NEologd (Japanese dictionary)

Tokenize with embedded ko-dic (Korean dictionary)

Tokenize with embedded CC-CEDICT (Chinese dictionary)

User dictionary examples

Use user dictionary (CSV format)

Use user dictionary (Binary format)

Tokenization modes

Normal mode (default)

Decompose mode

Output formats

MeCab format (default)

Wakati format

JSON format

Advanced tokenization

Tokenize with character and token filters

Dictionary Training (Experimental)

Training parameters

Basic workflow

1. Prepare training files

2. Train model

3. Training results

Export trained model to dictionary

Basic export usage

Export parameters

Output files

Complete workflow example

1. Train model

2. Export to dictionary format

3. Build dictionary

4. Use trained dictionary

Metadata update feature

API reference