%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 - 2.9.9-1 - Package Spec generated