path: root/python-unidic2ud.spec

%global _empty_manifest_terminate_build 0
Name:		python-unidic2ud
Version:	2.9.9
Release:	1
Summary:	Tokenizer POS-tagger Lemmatizer and Dependency-parser for modern and contemporary Japanese
License:	MIT
URL:		https://github.com/KoichiYasuoka/UniDic2UD
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/38/31/c632c61020e41c516022a0d60a588d5260578477733f5167a5e48f83afdd/unidic2ud-2.9.9.tar.gz
BuildArch:	noarch


%description
[![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.

%package -n python3-unidic2ud
Summary:	Tokenizer POS-tagger Lemmatizer and Dependency-parser for modern and contemporary Japanese
Provides:	python-unidic2ud
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-unidic2ud
[![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.

%package help
Summary:	Development documents and examples for unidic2ud
Provides:	python3-unidic2ud-doc
%description help
[![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.

%prep
%autosetup -n unidic2ud-2.9.9

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-unidic2ud -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 2.9.9-1
- Package Spec generated