| Name | fugashi-plus JSON |
| Version |
1.5.2.post1
JSON |
| download |
| home_page | None |
| Summary | Cython MeCab wrapper for fast, pythonic Japanese tokenization and morphological analysis with additional improvements. |
| upload_time | 2025-11-01 22:34:37 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# fugashi-plus
[](https://pypi.org/project/fugashi-plus/)
[](https://pypi.org/project/fugashi-plus/)
fugashi-plus は、主に Windows 対応や MeCab の [ikegami-yukino/mecab](https://github.com/ikegami-yukino/mecab) への移行などコードのメンテナンスを目的とした、[fugashi](https://github.com/polm/fugashi) の派生ライブラリです。
## Changes in this fork
- **パッケージ名を `fugashi-plus` に変更**
- ライブラリ名は `fugashi` から変更されておらず、[fugashi](https://github.com/polm/fugashi) 本家同様に `import fugashi` でインポートできる
- [fugashi](https://github.com/polm/fugashi) 本家のドロップイン代替として利用できる
- **明示的に Python 3.13 をサポート対象に追加**
- CI 対象の Python バージョンにも 3.13 を追加した
- **Cython を 3.0 系に更新**
- https://github.com/cython/cython/issues/5982 の通り、Python 3.13 では一部の非推奨 C API が削除されている
- Cython 0.x 系では Python 3.13 以降のビルドに失敗するため、Cython 3.0 系に更新した
- **Cython モジュールに型ヒント (Type Hints) を追加**
- fugashi 本家には型ヒントが同梱されておらず、また fugashi はほぼ 100% Cython で書かれているため、Pylance などでのコード補完や型チェックが全く効かない
- fugashi-plus では Cython モジュールに型ヒントを追加したことで、コード補完や型チェックが効くようになっている
- **MeCab を現在もメンテナンスが続けられている [ikegami-yukino/mecab](https://github.com/ikegami-yukino/mecab) に移行**
- **オリジナルの Mecab ([taku910/mecab](https://github.com/taku910/mecab)) は、2020 年以降メンテナンスが放棄されている**
- 大元の設計から Windows での使用を想定していないようで、Windows のサポートは不十分
- fugashi 本家では Windows 向け wheel のみ Windows 向けの修正を施した [chezou/mecab](https://github.com/chezou/mecab) が使われているが、2018 年以降メンテナンスが放棄されている
- **一方 [ikegami-yukino/mecab](https://github.com/ikegami-yukino/mecab) は現在でもメンテナンスが続けられており、Windows 64bit でも比較的容易にビルドできる**
- ただし、Visual Studio 2022 (Build Tools v143) では非推奨の一部 C++ 標準ライブラリが削除されている関係で、ビルドに失敗する
- このため、GitHub Actions のビルド環境では明示的に Build Tools v142 (Visual Studio 2019 相当) でビルドを行っている
- **[ikegami-yukino/mecab](https://github.com/ikegami-yukino/mecab) に移行することで、UniDic 2.3.0 以降でユーザー辞書のビルドに失敗する問題が修正される**
- 参考資料:
- https://github.com/taku910/mecab/issues/10
- https://github.com/polm/fugashi/issues/75
- https://ja.stackoverflow.com/a/74219/48588
- https://zenn.dev/zagvym/articles/28056236903369
- https://qiita.com/CookieBox26/items/a607d9e25f3b18d209ea
- 結局 MeCab 側の実装ミスかそれとも UniDic の作成不備かは釈然としないが、UniDic 側の作成不備だとすると 3.1.0 や最新版でも修正されていないのは不可解
- [ikegami-yukino/mecab](https://github.com/ikegami-yukino/mecab) ではこの問題を解決する https://github.com/taku910/mecab/pull/70 での修正内容が独自にマージされており、ユーザー辞書のビルドに失敗する問題が修正されている
- 上記プルリクエストでの修正を取り込まない場合、巨大な UniDic を手元で再ビルドするか、値決め打ちで UniDic の辞書データのバイナリを書き換えるしかなくなり、どちらの方法も実運用上非常に問題がある
- **Windows 環境において、システム辞書・ユーザー辞書の保存先パス指定が正常に機能しない問題を修正**
- fugashi 本家では `GenericTagger` クラス・`build_dictionary()` の両方で MeCab に渡す引数の分割に `shlex.split()` が使われていたが、shlex は Windows パスを正しく解釈しない
- fugashi-plus では、Windows のみ shlex を使わず独自に引数解析を行うことで、ライブラリユーザー側でワークアラウンドを挟むことなく、正常にシステム辞書・ユーザー辞書の保存先パスを指定できるようにしている
- **`Tagger` クラスのコンストラクタで、`unidic` / `unidic-lite` パッケージのインストール有無に関わらず、引数に指定されたシステム辞書を利用するよう変更**
- fugashi 本家では `unidic` / `unidic-lite` パッケージがインストール済みの環境だと、`Tagger` クラスのコンストラクタ引数 (`arg`) に独自にシステム辞書のパスを指定しても、常に `unidic` / `unidic-lite` パッケージ内蔵の UniDic が優先して利用されてしまっていた
- fugashi-plus では、コンストラクタ引数 (`arg`) の文字列内に `-r` や `-d` オプションが含まれない場合にのみ、`unidic` / `unidic-lite` パッケージに内蔵の UniDic を検出するロジックに変更している
- **その他コードのクリーンアップなど**
## Installation
下記コマンドを実行して、ライブラリをインストールできます。
```bash
pip install fugashi-plus
```
下記のドキュメントは、[fugashi](https://github.com/polm/fugashi) 本家のドキュメントを改変なしでそのまま引き継いでいます。
これらのドキュメントの内容が fugashi-plus にも通用するかは保証されません。
-------
[](https://fugashi.streamlit.app)
[](https://pypi.org/project/fugashi/)
[](https://pypi.org/project/fugashi/)
# fugashi
<img src="https://github.com/polm/fugashi/raw/main/fugashi.png" width=125 height=125 alt="fugashi by Irasutoya" />
fugashi is a Cython wrapper for [MeCab](https://taku910.github.io/mecab/), a
Japanese tokenizer and morphological analysis tool. Wheels are provided for
Linux, OSX (Intel), and Win64, and UniDic is [easy to install](#installing-a-dictionary).
**issueを英語で書く必要はありません。**
Check out the [interactive demo][], see the [blog post](https://www.dampfkraft.com/nlp/fugashi.html) for background
on why fugashi exists and some of the design decisions, or see [this
guide][guide] for a basic introduction to Japanese tokenization.
[guide]: https://www.dampfkraft.com/nlp/how-to-tokenize-japanese.html
[interactive demo]: https://fugashi.streamlit.app
If you are on a platform for which wheels are not provided, you'll need to
install MeCab first. It's recommended you install [from
source](https://github.com/taku910/mecab). If you need to build from source on
Windows, [@chezou's fork](https://github.com/chezou/mecab) is recommended; see
[issue #44](https://github.com/polm/fugashi/issues/44#issuecomment-954426115)
for an explanation of the problems with the official repo.
Known platforms without wheels:
- musl-based distros like alpine [#77](https://github.com/polm/fugashi/issues/77)
- PowerPC
- Windows 32bit
## Usage
```python
from fugashi import Tagger
tagger = Tagger('-Owakati')
text = "麩菓子は、麩を主材料とした日本の菓子。"
tagger.parse(text)
# => '麩 菓子 は 、 麩 を 主材 料 と し た 日本 の 菓子 。'
for word in tagger(text):
print(word, word.feature.lemma, word.pos, sep='\t')
# "feature" is the Unidic feature data as a named tuple
```
## Installing a Dictionary
fugashi requires a dictionary. [UniDic](https://unidic.ninjal.ac.jp/) is
recommended, and two easy-to-install versions are provided.
- [unidic-lite](https://github.com/polm/unidic-lite), a slightly modified version 2.1.2 of Unidic (from 2013) that's relatively small
- [unidic](https://github.com/polm/unidic-py), the latest UniDic 3.1.0, which is 770MB on disk and requires a separate download step
If you just want to make sure things work you can start with `unidic-lite`, but
for more serious processing `unidic` is recommended. For production use you'll
generally want to generate your own dictionary too; for details see the [MeCab
documentation](https://taku910.github.io/mecab/learn.html).
To get either of these dictionaries, you can install them directly using `pip`
or do the below:
```sh
pip install 'fugashi[unidic-lite]'
# The full version of UniDic requires a separate download step
pip install 'fugashi[unidic]'
python -m unidic download
```
For more information on the different MeCab dictionaries available, see [this article](https://www.dampfkraft.com/nlp/japanese-tokenizer-dictionaries.html).
## Dictionary Use
fugashi is written with the assumption you'll use Unidic to process Japanese,
but it supports arbitrary dictionaries.
If you're using a dictionary besides Unidic you can use the GenericTagger like this:
```python
from fugashi import GenericTagger
tagger = GenericTagger()
# parse can be used as normal
tagger.parse('something')
# features from the dictionary can be accessed by field numbers
for word in tagger(text):
print(word.surface, word.feature[0])
```
You can also create a dictionary wrapper to get feature information as a named tuple.
```python
from fugashi import GenericTagger, create_feature_wrapper
CustomFeatures = create_feature_wrapper('CustomFeatures', 'alpha beta gamma')
tagger = GenericTagger(wrapper=CustomFeatures)
for word in tagger.parseToNodeList(text):
print(word.surface, word.feature.alpha)
```
## Citation
If you use fugashi in research, it would be appreciated if you cite this paper. You can read it at [the ACL Anthology](https://www.aclweb.org/anthology/2020.nlposs-1.7/) or [on Arxiv](https://arxiv.org/abs/2010.06858).
@inproceedings{mccann-2020-fugashi,
title = "fugashi, a Tool for Tokenizing {J}apanese in Python",
author = "McCann, Paul",
booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.nlposs-1.7",
pages = "44--51",
abstract = "Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source tokenizers exist they can be hard to use and lack English documentation. This paper introduces fugashi, a MeCab wrapper for Python, and gives an introduction to tokenizing Japanese.",
}
## Alternatives
If you have a problem with fugashi feel free to open an issue. However, there
are some cases where it might be better to use a different library.
- If you don't want to deal with installing MeCab at all, try [SudachiPy](https://github.com/WorksApplications/sudachi.rs).
- If you need to work with Korean, try [pymecab-ko](https://github.com/NoUnique/pymecab-ko) or [KoNLPy](https://konlpy.org/en/latest/).
## License and Copyright Notice
fugashi is released under the terms of the [MIT license](./LICENSE). Please
copy it far and wide.
fugashi is a wrapper for MeCab, and fugashi wheels include MeCab binaries.
MeCab is copyrighted free software by Taku Kudo `<taku@chasen.org>` and Nippon
Telegraph and Telephone Corporation, and is redistributed under the [BSD
License](./LICENSE.mecab).
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"description": "# fugashi-plus\r\n\r\n[](https://pypi.org/project/fugashi-plus/)\r\n\r\n[](https://pypi.org/project/fugashi-plus/)\r\n\r\n\r\nfugashi-plus \u306f\u3001\u4e3b\u306b Windows \u5bfe\u5fdc\u3084 MeCab \u306e [ikegami-yukino/mecab](https://github.com/ikegami-yukino/mecab) \u3078\u306e\u79fb\u884c\u306a\u3069\u30b3\u30fc\u30c9\u306e\u30e1\u30f3\u30c6\u30ca\u30f3\u30b9\u3092\u76ee\u7684\u3068\u3057\u305f\u3001[fugashi](https://github.com/polm/fugashi) \u306e\u6d3e\u751f\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002\r\n\r\n## Changes in this fork\r\n\r\n- **\u30d1\u30c3\u30b1\u30fc\u30b8\u540d\u3092 `fugashi-plus` \u306b\u5909\u66f4**\r\n - \u30e9\u30a4\u30d6\u30e9\u30ea\u540d\u306f `fugashi` \u304b\u3089\u5909\u66f4\u3055\u308c\u3066\u304a\u3089\u305a\u3001[fugashi](https://github.com/polm/fugashi) \u672c\u5bb6\u540c\u69d8\u306b `import fugashi` \u3067\u30a4\u30f3\u30dd\u30fc\u30c8\u3067\u304d\u308b\r\n - [fugashi](https://github.com/polm/fugashi) \u672c\u5bb6\u306e\u30c9\u30ed\u30c3\u30d7\u30a4\u30f3\u4ee3\u66ff\u3068\u3057\u3066\u5229\u7528\u3067\u304d\u308b\r\n- **\u660e\u793a\u7684\u306b Python 3.13 \u3092\u30b5\u30dd\u30fc\u30c8\u5bfe\u8c61\u306b\u8ffd\u52a0**\r\n - CI \u5bfe\u8c61\u306e Python \u30d0\u30fc\u30b8\u30e7\u30f3\u306b\u3082 3.13 \u3092\u8ffd\u52a0\u3057\u305f\r\n- **Cython \u3092 3.0 \u7cfb\u306b\u66f4\u65b0**\r\n - https://github.com/cython/cython/issues/5982 \u306e\u901a\u308a\u3001Python 3.13 \u3067\u306f\u4e00\u90e8\u306e\u975e\u63a8\u5968 C API \u304c\u524a\u9664\u3055\u308c\u3066\u3044\u308b\r\n - Cython 0.x \u7cfb\u3067\u306f Python 3.13 \u4ee5\u964d\u306e\u30d3\u30eb\u30c9\u306b\u5931\u6557\u3059\u308b\u305f\u3081\u3001Cython 3.0 \u7cfb\u306b\u66f4\u65b0\u3057\u305f\r\n- **Cython \u30e2\u30b8\u30e5\u30fc\u30eb\u306b\u578b\u30d2\u30f3\u30c8 (Type Hints) \u3092\u8ffd\u52a0**\r\n - fugashi \u672c\u5bb6\u306b\u306f\u578b\u30d2\u30f3\u30c8\u304c\u540c\u68b1\u3055\u308c\u3066\u304a\u3089\u305a\u3001\u307e\u305f fugashi \u306f\u307b\u307c 100% Cython \u3067\u66f8\u304b\u308c\u3066\u3044\u308b\u305f\u3081\u3001Pylance \u306a\u3069\u3067\u306e\u30b3\u30fc\u30c9\u88dc\u5b8c\u3084\u578b\u30c1\u30a7\u30c3\u30af\u304c\u5168\u304f\u52b9\u304b\u306a\u3044\r\n - fugashi-plus \u3067\u306f Cython \u30e2\u30b8\u30e5\u30fc\u30eb\u306b\u578b\u30d2\u30f3\u30c8\u3092\u8ffd\u52a0\u3057\u305f\u3053\u3068\u3067\u3001\u30b3\u30fc\u30c9\u88dc\u5b8c\u3084\u578b\u30c1\u30a7\u30c3\u30af\u304c\u52b9\u304f\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u308b\r\n- **MeCab \u3092\u73fe\u5728\u3082\u30e1\u30f3\u30c6\u30ca\u30f3\u30b9\u304c\u7d9a\u3051\u3089\u308c\u3066\u3044\u308b [ikegami-yukino/mecab](https://github.com/ikegami-yukino/mecab) \u306b\u79fb\u884c**\r\n - **\u30aa\u30ea\u30b8\u30ca\u30eb\u306e Mecab ([taku910/mecab](https://github.com/taku910/mecab)) \u306f\u30012020 \u5e74\u4ee5\u964d\u30e1\u30f3\u30c6\u30ca\u30f3\u30b9\u304c\u653e\u68c4\u3055\u308c\u3066\u3044\u308b**\r\n - \u5927\u5143\u306e\u8a2d\u8a08\u304b\u3089 Windows \u3067\u306e\u4f7f\u7528\u3092\u60f3\u5b9a\u3057\u3066\u3044\u306a\u3044\u3088\u3046\u3067\u3001Windows \u306e\u30b5\u30dd\u30fc\u30c8\u306f\u4e0d\u5341\u5206\r\n - fugashi \u672c\u5bb6\u3067\u306f Windows \u5411\u3051 wheel \u306e\u307f Windows \u5411\u3051\u306e\u4fee\u6b63\u3092\u65bd\u3057\u305f [chezou/mecab](https://github.com/chezou/mecab) \u304c\u4f7f\u308f\u308c\u3066\u3044\u308b\u304c\u30012018 \u5e74\u4ee5\u964d\u30e1\u30f3\u30c6\u30ca\u30f3\u30b9\u304c\u653e\u68c4\u3055\u308c\u3066\u3044\u308b\r\n - **\u4e00\u65b9 [ikegami-yukino/mecab](https://github.com/ikegami-yukino/mecab) \u306f\u73fe\u5728\u3067\u3082\u30e1\u30f3\u30c6\u30ca\u30f3\u30b9\u304c\u7d9a\u3051\u3089\u308c\u3066\u304a\u308a\u3001Windows 64bit \u3067\u3082\u6bd4\u8f03\u7684\u5bb9\u6613\u306b\u30d3\u30eb\u30c9\u3067\u304d\u308b**\r\n - \u305f\u3060\u3057\u3001Visual Studio 2022 (Build Tools v143) \u3067\u306f\u975e\u63a8\u5968\u306e\u4e00\u90e8 C++ \u6a19\u6e96\u30e9\u30a4\u30d6\u30e9\u30ea\u304c\u524a\u9664\u3055\u308c\u3066\u3044\u308b\u95a2\u4fc2\u3067\u3001\u30d3\u30eb\u30c9\u306b\u5931\u6557\u3059\u308b\r\n - \u3053\u306e\u305f\u3081\u3001GitHub Actions \u306e\u30d3\u30eb\u30c9\u74b0\u5883\u3067\u306f\u660e\u793a\u7684\u306b Build Tools v142 (Visual Studio 2019 \u76f8\u5f53) \u3067\u30d3\u30eb\u30c9\u3092\u884c\u3063\u3066\u3044\u308b\r\n - **[ikegami-yukino/mecab](https://github.com/ikegami-yukino/mecab) \u306b\u79fb\u884c\u3059\u308b\u3053\u3068\u3067\u3001UniDic 2.3.0 \u4ee5\u964d\u3067\u30e6\u30fc\u30b6\u30fc\u8f9e\u66f8\u306e\u30d3\u30eb\u30c9\u306b\u5931\u6557\u3059\u308b\u554f\u984c\u304c\u4fee\u6b63\u3055\u308c\u308b**\r\n - \u53c2\u8003\u8cc7\u6599:\r\n - https://github.com/taku910/mecab/issues/10\r\n - https://github.com/polm/fugashi/issues/75\r\n - https://ja.stackoverflow.com/a/74219/48588\r\n - https://zenn.dev/zagvym/articles/28056236903369\r\n - https://qiita.com/CookieBox26/items/a607d9e25f3b18d209ea\r\n - \u7d50\u5c40 MeCab \u5074\u306e\u5b9f\u88c5\u30df\u30b9\u304b\u305d\u308c\u3068\u3082 UniDic \u306e\u4f5c\u6210\u4e0d\u5099\u304b\u306f\u91c8\u7136\u3068\u3057\u306a\u3044\u304c\u3001UniDic \u5074\u306e\u4f5c\u6210\u4e0d\u5099\u3060\u3068\u3059\u308b\u3068 3.1.0 \u3084\u6700\u65b0\u7248\u3067\u3082\u4fee\u6b63\u3055\u308c\u3066\u3044\u306a\u3044\u306e\u306f\u4e0d\u53ef\u89e3\r\n - [ikegami-yukino/mecab](https://github.com/ikegami-yukino/mecab) \u3067\u306f\u3053\u306e\u554f\u984c\u3092\u89e3\u6c7a\u3059\u308b https://github.com/taku910/mecab/pull/70 \u3067\u306e\u4fee\u6b63\u5185\u5bb9\u304c\u72ec\u81ea\u306b\u30de\u30fc\u30b8\u3055\u308c\u3066\u304a\u308a\u3001\u30e6\u30fc\u30b6\u30fc\u8f9e\u66f8\u306e\u30d3\u30eb\u30c9\u306b\u5931\u6557\u3059\u308b\u554f\u984c\u304c\u4fee\u6b63\u3055\u308c\u3066\u3044\u308b\r\n - \u4e0a\u8a18\u30d7\u30eb\u30ea\u30af\u30a8\u30b9\u30c8\u3067\u306e\u4fee\u6b63\u3092\u53d6\u308a\u8fbc\u307e\u306a\u3044\u5834\u5408\u3001\u5de8\u5927\u306a UniDic \u3092\u624b\u5143\u3067\u518d\u30d3\u30eb\u30c9\u3059\u308b\u304b\u3001\u5024\u6c7a\u3081\u6253\u3061\u3067 UniDic \u306e\u8f9e\u66f8\u30c7\u30fc\u30bf\u306e\u30d0\u30a4\u30ca\u30ea\u3092\u66f8\u304d\u63db\u3048\u308b\u3057\u304b\u306a\u304f\u306a\u308a\u3001\u3069\u3061\u3089\u306e\u65b9\u6cd5\u3082\u5b9f\u904b\u7528\u4e0a\u975e\u5e38\u306b\u554f\u984c\u304c\u3042\u308b\r\n- **Windows \u74b0\u5883\u306b\u304a\u3044\u3066\u3001\u30b7\u30b9\u30c6\u30e0\u8f9e\u66f8\u30fb\u30e6\u30fc\u30b6\u30fc\u8f9e\u66f8\u306e\u4fdd\u5b58\u5148\u30d1\u30b9\u6307\u5b9a\u304c\u6b63\u5e38\u306b\u6a5f\u80fd\u3057\u306a\u3044\u554f\u984c\u3092\u4fee\u6b63**\r\n - fugashi \u672c\u5bb6\u3067\u306f `GenericTagger` \u30af\u30e9\u30b9\u30fb`build_dictionary()` \u306e\u4e21\u65b9\u3067 MeCab \u306b\u6e21\u3059\u5f15\u6570\u306e\u5206\u5272\u306b `shlex.split()` \u304c\u4f7f\u308f\u308c\u3066\u3044\u305f\u304c\u3001shlex \u306f Windows \u30d1\u30b9\u3092\u6b63\u3057\u304f\u89e3\u91c8\u3057\u306a\u3044\r\n - fugashi-plus \u3067\u306f\u3001Windows \u306e\u307f shlex \u3092\u4f7f\u308f\u305a\u72ec\u81ea\u306b\u5f15\u6570\u89e3\u6790\u3092\u884c\u3046\u3053\u3068\u3067\u3001\u30e9\u30a4\u30d6\u30e9\u30ea\u30e6\u30fc\u30b6\u30fc\u5074\u3067\u30ef\u30fc\u30af\u30a2\u30e9\u30a6\u30f3\u30c9\u3092\u631f\u3080\u3053\u3068\u306a\u304f\u3001\u6b63\u5e38\u306b\u30b7\u30b9\u30c6\u30e0\u8f9e\u66f8\u30fb\u30e6\u30fc\u30b6\u30fc\u8f9e\u66f8\u306e\u4fdd\u5b58\u5148\u30d1\u30b9\u3092\u6307\u5b9a\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u3066\u3044\u308b\r\n- **`Tagger` \u30af\u30e9\u30b9\u306e\u30b3\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u3067\u3001`unidic` / `unidic-lite` \u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u6709\u7121\u306b\u95a2\u308f\u3089\u305a\u3001\u5f15\u6570\u306b\u6307\u5b9a\u3055\u308c\u305f\u30b7\u30b9\u30c6\u30e0\u8f9e\u66f8\u3092\u5229\u7528\u3059\u308b\u3088\u3046\u5909\u66f4**\r\n - fugashi \u672c\u5bb6\u3067\u306f `unidic` / `unidic-lite` \u30d1\u30c3\u30b1\u30fc\u30b8\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u6e08\u307f\u306e\u74b0\u5883\u3060\u3068\u3001`Tagger` \u30af\u30e9\u30b9\u306e\u30b3\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u5f15\u6570 (`arg`) \u306b\u72ec\u81ea\u306b\u30b7\u30b9\u30c6\u30e0\u8f9e\u66f8\u306e\u30d1\u30b9\u3092\u6307\u5b9a\u3057\u3066\u3082\u3001\u5e38\u306b `unidic` / `unidic-lite` \u30d1\u30c3\u30b1\u30fc\u30b8\u5185\u8535\u306e UniDic \u304c\u512a\u5148\u3057\u3066\u5229\u7528\u3055\u308c\u3066\u3057\u307e\u3063\u3066\u3044\u305f\r\n - fugashi-plus \u3067\u306f\u3001\u30b3\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u5f15\u6570 (`arg`) \u306e\u6587\u5b57\u5217\u5185\u306b `-r` \u3084 `-d` \u30aa\u30d7\u30b7\u30e7\u30f3\u304c\u542b\u307e\u308c\u306a\u3044\u5834\u5408\u306b\u306e\u307f\u3001`unidic` / `unidic-lite` \u30d1\u30c3\u30b1\u30fc\u30b8\u306b\u5185\u8535\u306e UniDic \u3092\u691c\u51fa\u3059\u308b\u30ed\u30b8\u30c3\u30af\u306b\u5909\u66f4\u3057\u3066\u3044\u308b\r\n- **\u305d\u306e\u4ed6\u30b3\u30fc\u30c9\u306e\u30af\u30ea\u30fc\u30f3\u30a2\u30c3\u30d7\u306a\u3069**\r\n\r\n## Installation\r\n\r\n\u4e0b\u8a18\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u3066\u3001\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u307e\u3059\u3002\r\n\r\n```bash\r\npip install fugashi-plus\r\n```\r\n\r\n\u4e0b\u8a18\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306f\u3001[fugashi](https://github.com/polm/fugashi) \u672c\u5bb6\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3092\u6539\u5909\u306a\u3057\u3067\u305d\u306e\u307e\u307e\u5f15\u304d\u7d99\u3044\u3067\u3044\u307e\u3059\u3002 \r\n\u3053\u308c\u3089\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306e\u5185\u5bb9\u304c fugashi-plus \u306b\u3082\u901a\u7528\u3059\u308b\u304b\u306f\u4fdd\u8a3c\u3055\u308c\u307e\u305b\u3093\u3002\r\n\r\n-------\r\n\r\n[](https://fugashi.streamlit.app)\r\n[](https://pypi.org/project/fugashi/)\r\n\r\n[](https://pypi.org/project/fugashi/)\r\n\r\n\r\n# fugashi\r\n\r\n<img src=\"https://github.com/polm/fugashi/raw/main/fugashi.png\" width=125 height=125 alt=\"fugashi by Irasutoya\" />\r\n\r\nfugashi is a Cython wrapper for [MeCab](https://taku910.github.io/mecab/), a\r\nJapanese tokenizer and morphological analysis tool. Wheels are provided for\r\nLinux, OSX (Intel), and Win64, and UniDic is [easy to install](#installing-a-dictionary).\r\n\r\n**issue\u3092\u82f1\u8a9e\u3067\u66f8\u304f\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002**\r\n\r\nCheck out the [interactive demo][], see the [blog post](https://www.dampfkraft.com/nlp/fugashi.html) for background\r\non why fugashi exists and some of the design decisions, or see [this\r\nguide][guide] for a basic introduction to Japanese tokenization.\r\n\r\n[guide]: https://www.dampfkraft.com/nlp/how-to-tokenize-japanese.html\r\n[interactive demo]: https://fugashi.streamlit.app\r\n\r\nIf you are on a platform for which wheels are not provided, you'll need to\r\ninstall MeCab first. It's recommended you install [from\r\nsource](https://github.com/taku910/mecab). If you need to build from source on\r\nWindows, [@chezou's fork](https://github.com/chezou/mecab) is recommended; see\r\n[issue #44](https://github.com/polm/fugashi/issues/44#issuecomment-954426115)\r\nfor an explanation of the problems with the official repo.\r\n\r\nKnown platforms without wheels:\r\n\r\n- musl-based distros like alpine [#77](https://github.com/polm/fugashi/issues/77)\r\n- PowerPC\r\n- Windows 32bit\r\n\r\n## Usage\r\n\r\n```python\r\nfrom fugashi import Tagger\r\n\r\ntagger = Tagger('-Owakati')\r\ntext = \"\u9ea9\u83d3\u5b50\u306f\u3001\u9ea9\u3092\u4e3b\u6750\u6599\u3068\u3057\u305f\u65e5\u672c\u306e\u83d3\u5b50\u3002\"\r\ntagger.parse(text)\r\n# => '\u9ea9 \u83d3\u5b50 \u306f \u3001 \u9ea9 \u3092 \u4e3b\u6750 \u6599 \u3068 \u3057 \u305f \u65e5\u672c \u306e \u83d3\u5b50 \u3002'\r\nfor word in tagger(text):\r\n print(word, word.feature.lemma, word.pos, sep='\\t')\r\n # \"feature\" is the Unidic feature data as a named tuple\r\n```\r\n\r\n## Installing a Dictionary\r\n\r\nfugashi requires a dictionary. [UniDic](https://unidic.ninjal.ac.jp/) is\r\nrecommended, and two easy-to-install versions are provided.\r\n\r\n - [unidic-lite](https://github.com/polm/unidic-lite), a slightly modified version 2.1.2 of Unidic (from 2013) that's relatively small\r\n - [unidic](https://github.com/polm/unidic-py), the latest UniDic 3.1.0, which is 770MB on disk and requires a separate download step\r\n\r\nIf you just want to make sure things work you can start with `unidic-lite`, but\r\nfor more serious processing `unidic` is recommended. For production use you'll\r\ngenerally want to generate your own dictionary too; for details see the [MeCab\r\ndocumentation](https://taku910.github.io/mecab/learn.html).\r\n\r\nTo get either of these dictionaries, you can install them directly using `pip`\r\nor do the below:\r\n\r\n```sh\r\npip install 'fugashi[unidic-lite]'\r\n\r\n# The full version of UniDic requires a separate download step\r\npip install 'fugashi[unidic]'\r\npython -m unidic download\r\n```\r\n\r\nFor more information on the different MeCab dictionaries available, see [this article](https://www.dampfkraft.com/nlp/japanese-tokenizer-dictionaries.html).\r\n\r\n## Dictionary Use\r\n\r\nfugashi is written with the assumption you'll use Unidic to process Japanese,\r\nbut it supports arbitrary dictionaries. \r\n\r\nIf you're using a dictionary besides Unidic you can use the GenericTagger like this:\r\n\r\n```python\r\nfrom fugashi import GenericTagger\r\ntagger = GenericTagger()\r\n\r\n# parse can be used as normal\r\ntagger.parse('something')\r\n# features from the dictionary can be accessed by field numbers\r\nfor word in tagger(text):\r\n print(word.surface, word.feature[0])\r\n```\r\n\r\nYou can also create a dictionary wrapper to get feature information as a named tuple. \r\n\r\n```python\r\nfrom fugashi import GenericTagger, create_feature_wrapper\r\nCustomFeatures = create_feature_wrapper('CustomFeatures', 'alpha beta gamma')\r\ntagger = GenericTagger(wrapper=CustomFeatures)\r\nfor word in tagger.parseToNodeList(text):\r\n print(word.surface, word.feature.alpha)\r\n```\r\n\r\n## Citation\r\n\r\nIf you use fugashi in research, it would be appreciated if you cite this paper. You can read it at [the ACL Anthology](https://www.aclweb.org/anthology/2020.nlposs-1.7/) or [on Arxiv](https://arxiv.org/abs/2010.06858).\r\n\r\n @inproceedings{mccann-2020-fugashi,\r\n title = \"fugashi, a Tool for Tokenizing {J}apanese in Python\",\r\n author = \"McCann, Paul\",\r\n booktitle = \"Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)\",\r\n month = nov,\r\n year = \"2020\",\r\n address = \"Online\",\r\n publisher = \"Association for Computational Linguistics\",\r\n url = \"https://www.aclweb.org/anthology/2020.nlposs-1.7\",\r\n pages = \"44--51\",\r\n abstract = \"Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source tokenizers exist they can be hard to use and lack English documentation. This paper introduces fugashi, a MeCab wrapper for Python, and gives an introduction to tokenizing Japanese.\",\r\n }\r\n\r\n## Alternatives\r\n\r\nIf you have a problem with fugashi feel free to open an issue. However, there\r\nare some cases where it might be better to use a different library.\r\n\r\n- If you don't want to deal with installing MeCab at all, try [SudachiPy](https://github.com/WorksApplications/sudachi.rs).\r\n- If you need to work with Korean, try [pymecab-ko](https://github.com/NoUnique/pymecab-ko) or [KoNLPy](https://konlpy.org/en/latest/).\r\n\r\n## License and Copyright Notice\r\n\r\nfugashi is released under the terms of the [MIT license](./LICENSE). Please\r\ncopy it far and wide.\r\n\r\nfugashi is a wrapper for MeCab, and fugashi wheels include MeCab binaries.\r\nMeCab is copyrighted free software by Taku Kudo `<taku@chasen.org>` and Nippon\r\nTelegraph and Telephone Corporation, and is redistributed under the [BSD\r\nLicense](./LICENSE.mecab).\r\n",
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