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https://github.com/koichiyasuoka/unidic2ud

Tokenizer POS-tagger Lemmatizer and Dependency-parser for modern and contemporary Japanese
https://github.com/koichiyasuoka/unidic2ud
dependency-parser japanese-language nlp
Last synced: 2 days ago
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Tokenizer POS-tagger Lemmatizer and Dependency-parser for modern and contemporary Japanese
Host: GitHub
URL: https://github.com/koichiyasuoka/unidic2ud
Owner: KoichiYasuoka
License: mit
Created: 2019-08-26T15:42:04.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2024-10-30T03:12:06.000Z (19 days ago)
Last Synced: 2024-10-30T06:14:53.798Z (19 days ago)
Topics: dependency-parser, japanese-language, nlp
Language: Python
Homepage:
Size: 160 MB
Stars: 34
Watchers: 2
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README

        [![Current PyPI packages](https://badge.fury.io/py/unidic2ud.svg)](https://pypi.org/project/unidic2ud/)

# UniDic2UD

Tokenizer, POS-tagger, lemmatizer, and dependency-parser for modern and contemporary Japanese, working on [Universal Dependencies](https://universaldependencies.org/format.html).

## Basic usage

```py

>>> import unidic2ud

>>> nlp=unidic2ud.load("kindai")

>>> s=nlp("其國を治めんと欲する者は先づ其家を齊ふ")

>>> print(s)

# text = 其國を治めんと欲する者は先づ其家を齊ふ

1	其	其の	DET	連体詞	_	2	det	_	SpaceAfter=No|Translit=ソノ

2	國	国	NOUN	名詞-普通名詞-一般	_	4	obj	_	SpaceAfter=No|Translit=クニ

3	を	を	ADP	助詞-格助詞	_	2	case	_	SpaceAfter=No|Translit=ヲ

4	治め	収める	VERB	動詞-一般	_	7	advcl	_	SpaceAfter=No|Translit=オサメ

5	ん	む	AUX	助動詞	_	4	aux	_	SpaceAfter=No|Translit=ン

6	と	と	ADP	助詞-格助詞	_	4	case	_	SpaceAfter=No|Translit=ト

7	欲する	欲する	VERB	動詞-一般	_	8	acl	_	SpaceAfter=No|Translit=ホッスル

8	者	者	NOUN	名詞-普通名詞-一般	_	14	nsubj	_	SpaceAfter=No|Translit=モノ

9	は	は	ADP	助詞-係助詞	_	8	case	_	SpaceAfter=No|Translit=ハ

10	先づ	先ず	ADV	副詞	_	14	advmod	_	SpaceAfter=No|Translit=マヅ

11	其	其の	DET	連体詞	_	12	det	_	SpaceAfter=No|Translit=ソノ

12	家	家	NOUN	名詞-普通名詞-一般	_	14	obj	_	SpaceAfter=No|Translit=ウチ

13	を	を	ADP	助詞-格助詞	_	12	case	_	SpaceAfter=No|Translit=ヲ

14	齊ふ	整える	VERB	動詞-一般	_	0	root	_	SpaceAfter=No|Translit=トトノフ

>>> t=s[7]

>>> print(t.id,t.form,t.lemma,t.upos,t.xpos,t.feats,t.head.id,t.deprel,t.deps,t.misc)

7 欲する 欲する VERB 動詞-一般 _ 8 acl _ SpaceAfter=No|Translit=ホッスル

>>> print(s.to_tree())

    其 <══╗         det(決定詞)

    國 ═╗═╝<╗       obj(目的語)

    を <╝   ║       case(格表示)

  治め ═╗═╗═╝<╗     advcl(連用修飾節)

    ん <╝ ║   ║     aux(動詞補助成分)

    と <══╝   ║     case(格表示)

欲する ═══════╝<╗   acl(連体修飾節)

    者 ═╗═══════╝<╗ nsubj(主語)

    は <╝         ║ case(格表示)

  先づ <══════╗   ║ advmod(連用修飾語)

    其 <══╗   ║   ║ det(決定詞)

    家 ═╗═╝<╗ ║   ║ obj(目的語)

    を <╝   ║ ║   ║ case(格表示)

  齊ふ ═════╝═╝═══╝ root(親)

>>> f=open("trial.svg","w")

>>> f.write(s.to_svg())

>>> f.close()

```

![trial.svg](https://raw.githubusercontent.com/KoichiYasuoka/UniDic2UD/master/trial.png)

`unidic2ud.load(UniDic,UDPipe)` loads a natural language processor pipeline, which uses `UniDic` for tokenizer POS-tagger and lemmatizer, then uses `UDPipe` for dependency-parser. The default `UDPipe` is `UDPipe="japanese-modern"`. Available `UniDic` options are:

* `UniDic="gendai"`: Use [現代書き言葉UniDic](https://clrd.ninjal.ac.jp/unidic/download_all.html#unidic_bccwj).

* `UniDic="spoken"`: Use [現代話し言葉UniDic](https://clrd.ninjal.ac.jp/unidic/download_all.html#unidic_csj).

* `UniDic="novel"`: Use [近現代口語小説UniDic](https://clrd.ninjal.ac.jp/unidic/download_all.html#unidic_novel).

* `UniDic="qkana"`: Use [旧仮名口語UniDic](https://clrd.ninjal.ac.jp/unidic/download_all.html#unidic_qkana).

* `UniDic="kindai"`: Use [近代文語UniDic](https://clrd.ninjal.ac.jp/unidic/download_all.html#unidic_kindai).

* `UniDic="kinsei"`: Use [近世江戸口語UniDic](https://clrd.ninjal.ac.jp/unidic/download_all.html#unidic_kinsei-edo).

* `UniDic="kyogen"`: Use [中世口語UniDic](https://clrd.ninjal.ac.jp/unidic/download_all.html#unidic_chusei-kougo).

* `UniDic="wakan"`: Use [中世文語UniDic](https://clrd.ninjal.ac.jp/unidic/download_all.html#unidic_chusei-bungo).

* `UniDic="wabun"`: Use [中古和文UniDic](https://clrd.ninjal.ac.jp/unidic/download_all.html#unidic_wabun).

* `UniDic="manyo"`: Use [上代語UniDic](https://clrd.ninjal.ac.jp/unidic/download_all.html#unidic_jodai).

* `UniDic=None`: Use `UDPipe` for tokenizer, POS-tagger, lemmatizer, and dependency-parser.

`unidic2ud.UniDic2UDEntry.to_tree()` has an option `to_tree(BoxDrawingWidth=2)` for old terminals, whose Box Drawing characters are "fullwidth".

You can simply use `unidic2ud` on the command line:

```sh

echo 其國を治めんと欲する者は先づ其家を齊ふ | unidic2ud -U kindai

```

## CaboCha emulator usage

```py

>>> import unidic2ud.cabocha as CaboCha

>>> c=CaboCha.Parser("kindai")

>>> s=c.parse("其國を治めんと欲する者は先づ其家を齊ふ")

>>> print(s.toString(CaboCha.FORMAT_TREE_LATTICE))

  其-D

  國を-D

治めんと-D

    欲する-D

        者は-------D

          先づ-----D

              其-D |

              家を-D

                齊ふ

EOS

* 0 1D 0/0 0.000000

其	連体詞,*,*,*,*,*,其の,ソノ,*,DET	O	1<-det-2

* 1 2D 0/1 0.000000

國	名詞,普通名詞,一般,*,*,*,国,クニ,*,NOUN	O	2<-obj-4

を	助詞,格助詞,*,*,*,*,を,ヲ,*,ADP	O	3<-case-2

* 2 3D 0/1 0.000000

治め	動詞,一般,*,*,*,*,収める,オサメ,*,VERB	O	4<-advcl-7

ん	助動詞,*,*,*,*,*,む,ン,*,AUX	O	5<-aux-4

と	助詞,格助詞,*,*,*,*,と,ト,*,ADP	O	6<-case-4

* 3 4D 0/0 0.000000

欲する	動詞,一般,*,*,*,*,欲する,ホッスル,*,VERB	O	7<-acl-8

* 4 8D 0/1 0.000000

者	名詞,普通名詞,一般,*,*,*,者,モノ,*,NOUN	O	8<-nsubj-14

は	助詞,係助詞,*,*,*,*,は,ハ,*,ADP	O	9<-case-8

* 5 8D 0/0 0.000000

先づ	副詞,*,*,*,*,*,先ず,マヅ,*,ADV	O	10<-advmod-14

* 6 7D 0/0 0.000000

其	連体詞,*,*,*,*,*,其の,ソノ,*,DET	O	11<-det-12

* 7 8D 0/1 0.000000

家	名詞,普通名詞,一般,*,*,*,家,ウチ,*,NOUN	O	12<-obj-14

を	助詞,格助詞,*,*,*,*,を,ヲ,*,ADP	O	13<-case-12

* 8 -1D 0/0 0.000000

齊ふ	動詞,一般,*,*,*,*,整える,トトノフ,*,VERB	O	14<-root

EOS

>>> for c in [s.chunk(i) for i in range(s.chunk_size())]:

...   if c.link>=0:

...     print(c,"->",s.chunk(c.link))

...

其 -> 國を

國を -> 治めんと

治めんと -> 欲する

欲する -> 者は

者は -> 齊ふ

先づ -> 齊ふ

其 -> 家を

家を -> 齊ふ

```

`CaboCha.Parser(UniDic)` is an alias for `unidic2ud.load(UniDic,UDPipe="japanese-modern")`, and its default is `UniDic=None`. `CaboCha.Tree.toString(format)` has five available formats:

* `CaboCha.FORMAT_TREE`: tree (numbered as 0)

* `CaboCha.FORMAT_LATTICE`: lattice (numbered as 1)

* `CaboCha.FORMAT_TREE_LATTICE`: tree + lattice (numbered as 2)

* `CaboCha.FORMAT_XML`: XML (numbered as 3)

* `CaboCha.FORMAT_CONLL`: Universal Dependencies CoNLL-U (numbered as 4)

You can simply use `udcabocha` on the command line:

```sh

echo 其國を治めんと欲する者は先づ其家を齊ふ | udcabocha -U kindai -f 2

```

`-U UniDic` specifies `UniDic`. `-f format` specifies the output format in 0 to 4 above (default is `-f 0`) and in 5 to 8 below:

* `-f 5`: `to_tree()`

* `-f 6`: `to_tree(BoxDrawingWidth=2)`

* `-f 7`: `to_svg()`

* `-f 8`: [raw DOT](https://graphviz.readthedocs.io/en/stable/manual.html#using-raw-dot) graph through [Immediate Catena Analysis](https://koichiyasuoka.github.io/deplacy/#deplacydot)

![dot.png](https://raw.githubusercontent.com/KoichiYasuoka/UniDic2UD/master/dot.png)

Try [notebook](https://colab.research.google.com/github/KoichiYasuoka/UniDic2UD/blob/master/udcabocha.ipynb) for Google Colaboratory.

## Usage via spaCy

If you have already installed [spaCy](https://pypi.org/project/spacy/) 2.1.0 or later, you can use `UniDic` via spaCy Language pipeline.

```py

>>> import unidic2ud.spacy

>>> nlp=unidic2ud.spacy.load("kindai")

>>> d=nlp("其國を治めんと欲する者は先づ其家を齊ふ")

>>> print(unidic2ud.spacy.to_conllu(d))

# text = 其國を治めんと欲する者は先づ其家を齊ふ

1	其	其の	DET	連体詞	_	2	det	_	SpaceAfter=No|Translit=ソノ

2	國	国	NOUN	名詞-普通名詞-一般	_	4	obj	_	SpaceAfter=No|Translit=クニ

3	を	を	ADP	助詞-格助詞	_	2	case	_	SpaceAfter=No|Translit=ヲ

4	治め	収める	VERB	動詞-一般	_	7	advcl	_	SpaceAfter=No|Translit=オサメ

5	ん	む	AUX	助動詞	_	4	aux	_	SpaceAfter=No|Translit=ン

6	と	と	ADP	助詞-格助詞	_	4	case	_	SpaceAfter=No|Translit=ト

7	欲する	欲する	VERB	動詞-一般	_	8	acl	_	SpaceAfter=No|Translit=ホッスル

8	者	者	NOUN	名詞-普通名詞-一般	_	14	nsubj	_	SpaceAfter=No|Translit=モノ

9	は	は	ADP	助詞-係助詞	_	8	case	_	SpaceAfter=No|Translit=ハ

10	先づ	先ず	ADV	副詞	_	14	advmod	_	SpaceAfter=No|Translit=マヅ

11	其	其の	DET	連体詞	_	12	det	_	SpaceAfter=No|Translit=ソノ

12	家	家	NOUN	名詞-普通名詞-一般	_	14	obj	_	SpaceAfter=No|Translit=ウチ

13	を	を	ADP	助詞-格助詞	_	12	case	_	SpaceAfter=No|Translit=ヲ

14	齊ふ	整える	VERB	動詞-一般	_	0	root	_	SpaceAfter=No|Translit=トトノフ

>>> t=d[6]

>>> print(t.i+1,t.orth_,t.lemma_,t.pos_,t.tag_,t.head.i+1,t.dep_,t.whitespace_,t.norm_)

7 欲する 欲する VERB 動詞-一般 8 acl  ホッスル

>>> from deplacy.deprelja import deprelja

>>> for b in unidic2ud.spacy.bunsetu_spans(d):

...   for t in b.lefts:

...     print(unidic2ud.spacy.bunsetu_span(t),"->",b,"("+deprelja[t.dep_]+")")

...

其 -> 國を (決定詞)

國を -> 治めんと (目的語)

治めんと -> 欲する (連用修飾節)

欲する -> 者は (連体修飾節)

其 -> 家を (決定詞)

者は -> 齊ふ (主語)

先づ -> 齊ふ (連用修飾語)

家を -> 齊ふ (目的語)

```

`unidic2ud.spacy.load(UniDic,parser)` loads a spaCy pipeline, which uses `UniDic` for tokenizer POS-tagger and lemmatizer (as shown above), then uses `parser` for dependency-parser. The default `parser` is `parser="japanese-modern"` and available options are:

* `parser="ja_core_news_sm"`: Use [spaCy Japanese model](https://spacy.io/models/ja) (small).

* `parser="ja_core_news_md"`: Use spaCy Japanese model (middle).

* `parser="ja_core_news_lg"`: Use spaCy Japanese model (large).

* `parser="ja_ginza"`: Use [GiNZA](https://github.com/megagonlabs/ginza).

* `parser="japanese-gsd"`: Use [UDPipe Japanese model](http://hdl.handle.net/11234/1-3131).

* `parser="stanza_ja"`: Use [Stanza Japanese model](https://stanfordnlp.github.io/stanza/available_models.html).

## Installation for Linux

Tar-ball is available for Linux, and is installed by default when you use `pip`:

```sh

pip install unidic2ud

```

By default installation, `UniDic` is invoked through Web APIs. If you want to invoke them locally and faster, you can download `UniDic` which you use just as follows:

```sh

python -m unidic2ud download kindai

python -m unidic2ud dictlist

```

Licenses of dictionaries and models are: GPL/LGPL/BSD for `gendai` and `spoken`; CC BY-NC-SA 4.0 for others.

## Installation for Cygwin

Make sure to get `gcc-g++` `python37-pip` `python37-devel` packages, and then:

```sh

pip3.7 install unidic2ud

```

Use `python3.7` command in [Cygwin](https://www.cygwin.com/install.html) instead of `python`.

## Installation for Jupyter Notebook (Google Colaboratory)

```py

!pip install unidic2ud

```

## Benchmarks

Results of [舞姬/雪國/荒野より-Benchmarks](https://colab.research.google.com/github/KoichiYasuoka/UniDic2UD/blob/master/benchmark/benchmark.ipynb)

|[舞姬](https://github.com/KoichiYasuoka/UniDic2UD/blob/master/benchmark/maihime-benchmark.tar.gz)|LAS|MLAS|BLEX|

|---------------|-----|-----|-----|

|UniDic="kindai"|81.13|70.37|77.78|

|UniDic="qkana" |79.25|70.37|77.78|

|UniDic="kinsei"|72.22|60.71|64.29|

|[雪國](https://github.com/KoichiYasuoka/UniDic2UD/blob/master/benchmark/yukiguni-benchmark.tar.gz)|LAS|MLAS|BLEX|

|---------------|-----|-----|-----|

|UniDic="qkana" |89.29|85.71|81.63|

|UniDic="kinsei"|89.29|85.71|77.55|

|UniDic="kindai"|84.96|81.63|77.55|

|[荒野より](https://github.com/KoichiYasuoka/UniDic2UD/blob/master/benchmark/koyayori-benchmark.tar.gz)|LAS|MLAS|BLEX|

|---------------|-----|-----|-----|

|UniDic="kindai"|76.44|61.54|53.85|

|UniDic="qkana" |75.39|61.54|53.85|

|UniDic="kinsei"|71.88|58.97|51.28|

## Author

Koichi Yasuoka (安岡孝一)

## References

* 安岡孝一: [形態素解析部の付け替えによる近代日本語(旧字旧仮名)の係り受け解析](http://hdl.handle.net/2433/254677), 情報処理学会研究報告, Vol.2020-CH-124「人文科学とコンピュータ」, No.3 (2020年9月5日), pp.1-8.

* 安岡孝一: [漢日英Universal Dependencies平行コーパスとその差異](http://hdl.handle.net/2433/245218), 人文科学とコンピュータシンポジウム「じんもんこん2019」論文集 (2019年12月), pp.43-50.