SudachiPy 0.3.6

Python version of Sudachi, the Japanese Morphological Analyzer

SudachiPy

SudachiPy is a Python version of Sudachi, a Japanese morphological analyzer.

Sudachi & SudachiPy are developed in WAP Tokushima Laboratory of AI and NLP, an institute under Works Applications that focuses on Natural Language Processing (NLP).

Warning: SudachiPy is still under development, and some of the functions are still not complete. Please use it at your own risk.

Breaking changes

v0.3.0

• resources/ directory was moved to sudachipy/.

V0.2.2

• Distribute SudachiPy package via PyPI
  • pip install SudachiPy

v0.2.0

• User dictionary feature added

Easy Setup

SudachiPy requires Python3.5+.

Step 1: Install SudachiPy

SudachiPy is distributed from PyPI. You can install SudachiPy by executing pip install SudachiPy from the command line.

$ pip install SudachiPy

SudachiPy(>=v0.3.0) refers to system.dic of SudachiDict_core (not included in SudachiPy) package by default. Please proceed to Step 2 to install the dict package.

Step 2: Install SudachiDict_core

The default dict package SudachiDict_core is distributed from our download site. Run pip install like below:

$ pip install https://object-storage.tyo2.conoha.io/v1/nc_2520839e1f9641b08211a5c85243124a/sudachi/SudachiDict_core-20190718.tar.gz

Usage

As a command

After installing SudachiPy, you may also use it in the terminal via command sudachipy.

You can excute sudachipy with standard input by this way:

$ sudachipy

sudachipy has 4 subcommands (in default tokenize)

$ sudachipy tokenize -h
usage: sudachipy tokenize [-h] [-r file] [-m {A,B,C}] [-o file] [-a] [-d] [-v]
                          [file [file ...]]

Tokenize Text

positional arguments:
  file           text written in utf-8

optional arguments:
  -h, --help     show this help message and exit
  -r file        the setting file in JSON format
  -m {A,B,C}     the mode of splitting
  -o file        the output file
  -a             print all of the fields
  -d             print the debug information
  -v, --version  print sudachipy version

$ sudachipy link -h
usage: sudachipy link [-h] [-t {small,core,full}] [-u]

Link Default Dict Package

optional arguments:
  -h, --help            show this help message and exit
  -t {small,core,full}  dict dict
  -u                    unlink sudachidict

$ sudachipy build -h
usage: sudachipy build [-h] [-o file] [-d string] -m file file [file ...]

Build Sudachi Dictionary

positional arguments:
  file        source files with CSV format (one of more)

optional arguments:
  -h, --help  show this help message and exit
  -o file     output file (default: system.dic)
  -d string   description comment to be embedded on dictionary

required named arguments:
  -m file     connection matrix file with MeCab's matrix.def format

$ sudachipy ubuild -h
usage: sudachipy ubuild [-h] [-d string] [-o file] [-s file] file [file ...]

Build User Dictionary

positional arguments:
  file        source files with CSV format (one or more)

optional arguments:
  -h, --help  show this help message and exit
  -d string   description comment to be embedded on dictionary
  -o file     output file (default: user.dic)
  -s file     system dictionary (default: ${SUDACHIPY}/resouces/system.dic)

As a Python package

Here is an example usage;

from sudachipy import tokenizer
from sudachipy import dictionary


tokenizer_obj = dictionary.Dictionary().create()


# Multi-granular tokenization
# (following results are w/ `system_full.dic`
# you may not be able to replicate this particular example w/ `system_core.dic`)

mode = tokenizer.Tokenizer.SplitMode.C
[m.surface() for m in tokenizer_obj.tokenize("医薬品安全管理責任者", mode)]
# => ['医薬品安全管理責任者']

mode = tokenizer.Tokenizer.SplitMode.B
[m.surface() for m in tokenizer_obj.tokenize("医薬品安全管理責任者", mode)]
# => ['医薬品', '安全', '管理', '責任者']

mode = tokenizer.Tokenizer.SplitMode.A
[m.surface() for m in tokenizer_obj.tokenize("医薬品安全管理責任者", mode)]
# => ['医薬', '品', '安全', '管理', '責任', '者']


# Morpheme information

m = tokenizer_obj.tokenize("食べ", mode)[0]

m.surface() # => '食べ'
m.dictionary_form() # => '食べる'
m.reading_form() # => 'タベ'
m.part_of_speech() # => ['動詞', '一般', '*', '*', '下一段-バ行', '連用形-一般']


# Normalization

tokenizer_obj.tokenize("附属", mode)[0].normalized_form()
# => '付属'
tokenizer_obj.tokenize("SUMMER", mode)[0].normalized_form()
# => 'サマー'
tokenizer_obj.tokenize("シュミレーション", mode)[0].normalized_form()
# => 'シミュレーション'

Install dict packages

You can download and install the built dictionaries from Python packages · WorksApplications/SudachiDict.

$ pip install SudachiDict_full-20190531.tar.gz

You can change the default dict package by executing link command.

$ sudachipy link -t full

You can remove default dict setting.

$ sudachipy link -u

Customized dictionary

If you need to apply customized system.dic, place sudachi.json to anywhere you like, and overwrite systemDict value with the relative path from sudachi.json to your system.dic.

{
    "systemDict" : "relative/path/to/system.dic",
    ...
}

Then you can specify sudachi.json with -r option.

$ sudachipy -r path/to/sudachi.json

In the end, we would like to make a flow to get these resources via the code, like NLTK (e.g., import nltk; nltk.download()) or spaCy (e.g., $python -m spacy download en).

For developer

Code format

You can use ./scripts/format.sh and check if your code is in rule. flake8 flake8-import-order flake8-buitins is required. See requirements.txt

Test

You can use ./script/test.sh and check if not your change cause regression.