Metadata-Version: 2.1
Name: unidic2ud
Version: 2.8.5
Summary: Tokenizer POS-tagger Lemmatizer and Dependency-parser for modern and contemporary Japanese
Home-page: https://github.com/KoichiYasuoka/UniDic2UD
Author: Koichi Yasuoka
Author-email: yasuoka@kanji.zinbun.kyoto-u.ac.jp
License: MIT
Project-URL: japanese-modern, https://github.com/UniversalDependencies/UD_Japanese-Modern
Project-URL: ud-ja-kanbun, https://corpus.kanji.zinbun.kyoto-u.ac.jp/gitlab/Kanbun/ud-ja-kanbun
Project-URL: Source, https://github.com/KoichiYasuoka/UniDic2UD
Project-URL: Tracker, https://github.com/KoichiYasuoka/UniDic2UD/issues
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
        >>> qkana=unidic2ud.load("qkana")
        >>> s=qkana("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://unidic.ninjal.ac.jp/download#unidic_bccwj).
        * `UniDic="spoken"`: Use [現代話し言葉UniDic](https://unidic.ninjal.ac.jp/download#unidic_csj).
        * `UniDic="qkana"`: Use [旧仮名口語UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_qkana).
        * `UniDic="kindai"`: Use [近代文語UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_kindai).
        * `UniDic="kinsei"`: Use [近世口語（洒落本）UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_kinsei).
        * `UniDic="kyogen"`: Use [中世口語（狂言）UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_kyogen).
        * `UniDic="wakan"`: Use [中世文語（説話・随筆）UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_wakan).
        * `UniDic="wabun"`: Use [中古和文UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_wabun).
        * `UniDic="manyo"`: Use [上代（万葉集）UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_manyo).
        * `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 qkana
        ```
        
        ## CaboCha emulator usage
        
        ```py
        >>> import unidic2ud.cabocha as CaboCha
        >>> qkana=CaboCha.Parser("qkana")
        >>> s=qkana.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 qkana -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
        >>> qkana=unidic2ud.spacy.load("qkana")
        >>> d=qkana("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 qkana
        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.
        
Keywords: unidic udpipe mecab nlp
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Natural Language :: Japanese
Requires-Python: >=3.6
Description-Content-Type: text/markdown
