%global _empty_manifest_terminate_build 0 Name: python-udkanbun Version: 3.3.7 Release: 1 Summary: Tokenizer POS-tagger and Dependency-parser for Classical Chinese License: MIT URL: https://github.com/KoichiYasuoka/UD-Kanbun Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b8/fa/1725e688be150918a02652c9551543bd248c312840771913dee1a3ed6249/udkanbun-3.3.7.tar.gz BuildArch: noarch %description [![Current PyPI packages](https://badge.fury.io/py/udkanbun.svg)](https://pypi.org/project/udkanbun/) # UD-Kanbun Tokenizer, POS-Tagger, and Dependency-Parser for Classical Chinese Texts (漢文/文言文), working on [Universal Dependencies](https://universaldependencies.org/format.html). ## Basic usage ```py >>> import udkanbun >>> lzh=udkanbun.load() >>> s=lzh("不入虎穴不得虎子") >>> print(s) # text = 不入虎穴不得虎子 1 不 不 ADV v,副詞,否定,無界 Polarity=Neg 2 advmod _ Gloss=not|SpaceAfter=No 2 入 入 VERB v,動詞,行為,移動 _ 0 root _ Gloss=enter|SpaceAfter=No 3 虎 虎 NOUN n,名詞,主体,動物 _ 4 nmod _ Gloss=tiger|SpaceAfter=No 4 穴 穴 NOUN n,名詞,固定物,地形 Case=Loc 2 obj _ Gloss=cave|SpaceAfter=No 5 不 不 ADV v,副詞,否定,無界 Polarity=Neg 6 advmod _ Gloss=not|SpaceAfter=No 6 得 得 VERB v,動詞,行為,得失 _ 2 parataxis _ Gloss=get|SpaceAfter=No 7 虎 虎 NOUN n,名詞,主体,動物 _ 8 nmod _ Gloss=tiger|SpaceAfter=No 8 子 子 NOUN n,名詞,人,関係 _ 6 obj _ Gloss=child|SpaceAfter=No >>> t=s[1] >>> print(t.id,t.form,t.lemma,t.upos,t.xpos,t.feats,t.head.id,t.deprel,t.deps,t.misc) 1 不 不 ADV v,副詞,否定,無界 Polarity=Neg 2 advmod _ Gloss=not|SpaceAfter=No >>> print(s.kaeriten()) 不㆑入㆓虎穴㆒不㆑得㆓虎子㆒ >>> print(s.to_tree()) 不 <════╗ advmod 入 ═══╗═╝═╗ root 虎 <╗ ║ ║ nmod 穴 ═╝<╝ ║ obj 不 <════╗ ║ advmod 得 ═══╗═╝<╝ parataxis 虎 <╗ ║ nmod 子 ═╝<╝ obj >>> f=open("trial.svg","w") >>> f.write(s.to_svg()) >>> f.close() ``` ![trial.svg](https://raw.githubusercontent.com/KoichiYasuoka/UD-Kanbun/master/trial.png) `udkanbun.load()` has three options `udkanbun.load(MeCab=True,Danku=False)`. By default, the UD-Kanbun pipeline uses [MeCab](https://taku910.github.io/mecab/) for tokenizer and POS-tagger, then uses [UDPipe](http://ufal.mff.cuni.cz/udpipe) for dependency-parser. With the option `MeCab=False` the pipeline uses UDPipe for all through the processing. With the option `Danku=True` the pipeline tries to segment sentences automatically. `udkanbun.UDKanbunEntry.to_tree()` has an option `to_tree(BoxDrawingWidth=2)` for old terminals, whose Box Drawing characters are "fullwidth". `to_tree(kaeriten=True,Japanese=True)` is convenient for Japanese users. You can simply use `udkanbun` on the command line: ```sh echo 不入虎穴不得虎子 | udkanbun ``` ## Usage via spaCy If you have already installed [spaCy](https://pypi.org/project/spacy/) 2.1.0 or later, you can use UD-Kanbun via spaCy Language pipeline. ```py >>> import udkanbun.spacy >>> lzh=udkanbun.spacy.load() >>> d=lzh("不入虎穴不得虎子") >>> print(type(d)) >>> print(udkanbun.spacy.to_conllu(d)) # text = 不入虎穴不得虎子 1 不 不 ADV v,副詞,否定,無界 _ 2 advmod _ Gloss=not|SpaceAfter=No 2 入 入 VERB v,動詞,行為,移動 _ 0 root _ Gloss=enter|SpaceAfter=No 3 虎 虎 NOUN n,名詞,主体,動物 _ 4 nmod _ Gloss=tiger|SpaceAfter=No 4 穴 穴 NOUN n,名詞,固定物,地形 _ 2 obj _ Gloss=cave|SpaceAfter=No 5 不 不 ADV v,副詞,否定,無界 _ 6 advmod _ Gloss=not|SpaceAfter=No 6 得 得 VERB v,動詞,行為,得失 _ 2 parataxis _ Gloss=get|SpaceAfter=No 7 虎 虎 NOUN n,名詞,主体,動物 _ 8 nmod _ Gloss=tiger|SpaceAfter=No 8 子 子 NOUN n,名詞,人,関係 _ 6 obj _ Gloss=child|SpaceAfter=No >>> t=d[0] >>> print(t.i+1,t.orth_,t.lemma_,t.pos_,t.tag_,t.head.i+1,t.dep_,t.whitespace_,t.norm_) 1 不 不 ADV v,副詞,否定,無界 2 advmod not ``` ## Installation for Linux Tar-ball is available for Linux, and is installed by default when you use `pip`: ```sh pip install udkanbun ``` ## Installation for Cygwin Make sure to get `gcc-g++` `python37-pip` `python37-devel` packages, and then: ```sh pip3.7 install udkanbun ``` Use `python3.7` command in [Cygwin](https://www.cygwin.com/install.html) instead of `python`. ## Installation for Jupyter Notebook (Google Colaboratory) ```py !pip install udkanbun ``` Try [notebook](https://colab.research.google.com/github/KoichiYasuoka/UD-Kanbun/blob/master/udkanbun.ipynb) for Google Colaboratory. ## Author Koichi Yasuoka (安岡孝一) ## References * Koichi Yasuoka: [Universal Dependencies Treebank of the Four Books in Classical Chinese](http://hdl.handle.net/2433/245217), DADH2019: 10th International Conference of Digital Archives and Digital Humanities (December 2019), pp.20-28. * 安岡孝一: [四書を学んだMeCab＋UDPipeはセンター試験の漢文を読めるのか](http://hdl.handle.net/2433/237383), 東洋学へのコンピュータ利用, 第30回研究セミナー (2019年3月8日), pp.3-110. * 安岡孝一: [漢文の依存文法解析と返り点の関係について](http://hdl.handle.net/2433/235609), 日本漢字学会第1回研究大会予稿集 (2018年12月1日), pp.33-48. %package -n python3-udkanbun Summary: Tokenizer POS-tagger and Dependency-parser for Classical Chinese Provides: python-udkanbun BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-udkanbun [![Current PyPI packages](https://badge.fury.io/py/udkanbun.svg)](https://pypi.org/project/udkanbun/) # UD-Kanbun Tokenizer, POS-Tagger, and Dependency-Parser for Classical Chinese Texts (漢文/文言文), working on [Universal Dependencies](https://universaldependencies.org/format.html). ## Basic usage ```py >>> import udkanbun >>> lzh=udkanbun.load() >>> s=lzh("不入虎穴不得虎子") >>> print(s) # text = 不入虎穴不得虎子 1 不 不 ADV v,副詞,否定,無界 Polarity=Neg 2 advmod _ Gloss=not|SpaceAfter=No 2 入 入 VERB v,動詞,行為,移動 _ 0 root _ Gloss=enter|SpaceAfter=No 3 虎 虎 NOUN n,名詞,主体,動物 _ 4 nmod _ Gloss=tiger|SpaceAfter=No 4 穴 穴 NOUN n,名詞,固定物,地形 Case=Loc 2 obj _ Gloss=cave|SpaceAfter=No 5 不 不 ADV v,副詞,否定,無界 Polarity=Neg 6 advmod _ Gloss=not|SpaceAfter=No 6 得 得 VERB v,動詞,行為,得失 _ 2 parataxis _ Gloss=get|SpaceAfter=No 7 虎 虎 NOUN n,名詞,主体,動物 _ 8 nmod _ Gloss=tiger|SpaceAfter=No 8 子 子 NOUN n,名詞,人,関係 _ 6 obj _ Gloss=child|SpaceAfter=No >>> t=s[1] >>> print(t.id,t.form,t.lemma,t.upos,t.xpos,t.feats,t.head.id,t.deprel,t.deps,t.misc) 1 不 不 ADV v,副詞,否定,無界 Polarity=Neg 2 advmod _ Gloss=not|SpaceAfter=No >>> print(s.kaeriten()) 不㆑入㆓虎穴㆒不㆑得㆓虎子㆒ >>> print(s.to_tree()) 不 <════╗ advmod 入 ═══╗═╝═╗ root 虎 <╗ ║ ║ nmod 穴 ═╝<╝ ║ obj 不 <════╗ ║ advmod 得 ═══╗═╝<╝ parataxis 虎 <╗ ║ nmod 子 ═╝<╝ obj >>> f=open("trial.svg","w") >>> f.write(s.to_svg()) >>> f.close() ``` ![trial.svg](https://raw.githubusercontent.com/KoichiYasuoka/UD-Kanbun/master/trial.png) `udkanbun.load()` has three options `udkanbun.load(MeCab=True,Danku=False)`. By default, the UD-Kanbun pipeline uses [MeCab](https://taku910.github.io/mecab/) for tokenizer and POS-tagger, then uses [UDPipe](http://ufal.mff.cuni.cz/udpipe) for dependency-parser. With the option `MeCab=False` the pipeline uses UDPipe for all through the processing. With the option `Danku=True` the pipeline tries to segment sentences automatically. `udkanbun.UDKanbunEntry.to_tree()` has an option `to_tree(BoxDrawingWidth=2)` for old terminals, whose Box Drawing characters are "fullwidth". `to_tree(kaeriten=True,Japanese=True)` is convenient for Japanese users. You can simply use `udkanbun` on the command line: ```sh echo 不入虎穴不得虎子 | udkanbun ``` ## Usage via spaCy If you have already installed [spaCy](https://pypi.org/project/spacy/) 2.1.0 or later, you can use UD-Kanbun via spaCy Language pipeline. ```py >>> import udkanbun.spacy >>> lzh=udkanbun.spacy.load() >>> d=lzh("不入虎穴不得虎子") >>> print(type(d)) >>> print(udkanbun.spacy.to_conllu(d)) # text = 不入虎穴不得虎子 1 不 不 ADV v,副詞,否定,無界 _ 2 advmod _ Gloss=not|SpaceAfter=No 2 入 入 VERB v,動詞,行為,移動 _ 0 root _ Gloss=enter|SpaceAfter=No 3 虎 虎 NOUN n,名詞,主体,動物 _ 4 nmod _ Gloss=tiger|SpaceAfter=No 4 穴 穴 NOUN n,名詞,固定物,地形 _ 2 obj _ Gloss=cave|SpaceAfter=No 5 不 不 ADV v,副詞,否定,無界 _ 6 advmod _ Gloss=not|SpaceAfter=No 6 得 得 VERB v,動詞,行為,得失 _ 2 parataxis _ Gloss=get|SpaceAfter=No 7 虎 虎 NOUN n,名詞,主体,動物 _ 8 nmod _ Gloss=tiger|SpaceAfter=No 8 子 子 NOUN n,名詞,人,関係 _ 6 obj _ Gloss=child|SpaceAfter=No >>> t=d[0] >>> print(t.i+1,t.orth_,t.lemma_,t.pos_,t.tag_,t.head.i+1,t.dep_,t.whitespace_,t.norm_) 1 不 不 ADV v,副詞,否定,無界 2 advmod not ``` ## Installation for Linux Tar-ball is available for Linux, and is installed by default when you use `pip`: ```sh pip install udkanbun ``` ## Installation for Cygwin Make sure to get `gcc-g++` `python37-pip` `python37-devel` packages, and then: ```sh pip3.7 install udkanbun ``` Use `python3.7` command in [Cygwin](https://www.cygwin.com/install.html) instead of `python`. ## Installation for Jupyter Notebook (Google Colaboratory) ```py !pip install udkanbun ``` Try [notebook](https://colab.research.google.com/github/KoichiYasuoka/UD-Kanbun/blob/master/udkanbun.ipynb) for Google Colaboratory. ## Author Koichi Yasuoka (安岡孝一) ## References * Koichi Yasuoka: [Universal Dependencies Treebank of the Four Books in Classical Chinese](http://hdl.handle.net/2433/245217), DADH2019: 10th International Conference of Digital Archives and Digital Humanities (December 2019), pp.20-28. * 安岡孝一: [四書を学んだMeCab＋UDPipeはセンター試験の漢文を読めるのか](http://hdl.handle.net/2433/237383), 東洋学へのコンピュータ利用, 第30回研究セミナー (2019年3月8日), pp.3-110. * 安岡孝一: [漢文の依存文法解析と返り点の関係について](http://hdl.handle.net/2433/235609), 日本漢字学会第1回研究大会予稿集 (2018年12月1日), pp.33-48. %package help Summary: Development documents and examples for udkanbun Provides: python3-udkanbun-doc %description help [![Current PyPI packages](https://badge.fury.io/py/udkanbun.svg)](https://pypi.org/project/udkanbun/) # UD-Kanbun Tokenizer, POS-Tagger, and Dependency-Parser for Classical Chinese Texts (漢文/文言文), working on [Universal Dependencies](https://universaldependencies.org/format.html). ## Basic usage ```py >>> import udkanbun >>> lzh=udkanbun.load() >>> s=lzh("不入虎穴不得虎子") >>> print(s) # text = 不入虎穴不得虎子 1 不 不 ADV v,副詞,否定,無界 Polarity=Neg 2 advmod _ Gloss=not|SpaceAfter=No 2 入 入 VERB v,動詞,行為,移動 _ 0 root _ Gloss=enter|SpaceAfter=No 3 虎 虎 NOUN n,名詞,主体,動物 _ 4 nmod _ Gloss=tiger|SpaceAfter=No 4 穴 穴 NOUN n,名詞,固定物,地形 Case=Loc 2 obj _ Gloss=cave|SpaceAfter=No 5 不 不 ADV v,副詞,否定,無界 Polarity=Neg 6 advmod _ Gloss=not|SpaceAfter=No 6 得 得 VERB v,動詞,行為,得失 _ 2 parataxis _ Gloss=get|SpaceAfter=No 7 虎 虎 NOUN n,名詞,主体,動物 _ 8 nmod _ Gloss=tiger|SpaceAfter=No 8 子 子 NOUN n,名詞,人,関係 _ 6 obj _ Gloss=child|SpaceAfter=No >>> t=s[1] >>> print(t.id,t.form,t.lemma,t.upos,t.xpos,t.feats,t.head.id,t.deprel,t.deps,t.misc) 1 不 不 ADV v,副詞,否定,無界 Polarity=Neg 2 advmod _ Gloss=not|SpaceAfter=No >>> print(s.kaeriten()) 不㆑入㆓虎穴㆒不㆑得㆓虎子㆒ >>> print(s.to_tree()) 不 <════╗ advmod 入 ═══╗═╝═╗ root 虎 <╗ ║ ║ nmod 穴 ═╝<╝ ║ obj 不 <════╗ ║ advmod 得 ═══╗═╝<╝ parataxis 虎 <╗ ║ nmod 子 ═╝<╝ obj >>> f=open("trial.svg","w") >>> f.write(s.to_svg()) >>> f.close() ``` ![trial.svg](https://raw.githubusercontent.com/KoichiYasuoka/UD-Kanbun/master/trial.png) `udkanbun.load()` has three options `udkanbun.load(MeCab=True,Danku=False)`. By default, the UD-Kanbun pipeline uses [MeCab](https://taku910.github.io/mecab/) for tokenizer and POS-tagger, then uses [UDPipe](http://ufal.mff.cuni.cz/udpipe) for dependency-parser. With the option `MeCab=False` the pipeline uses UDPipe for all through the processing. With the option `Danku=True` the pipeline tries to segment sentences automatically. `udkanbun.UDKanbunEntry.to_tree()` has an option `to_tree(BoxDrawingWidth=2)` for old terminals, whose Box Drawing characters are "fullwidth". `to_tree(kaeriten=True,Japanese=True)` is convenient for Japanese users. You can simply use `udkanbun` on the command line: ```sh echo 不入虎穴不得虎子 | udkanbun ``` ## Usage via spaCy If you have already installed [spaCy](https://pypi.org/project/spacy/) 2.1.0 or later, you can use UD-Kanbun via spaCy Language pipeline. ```py >>> import udkanbun.spacy >>> lzh=udkanbun.spacy.load() >>> d=lzh("不入虎穴不得虎子") >>> print(type(d)) >>> print(udkanbun.spacy.to_conllu(d)) # text = 不入虎穴不得虎子 1 不 不 ADV v,副詞,否定,無界 _ 2 advmod _ Gloss=not|SpaceAfter=No 2 入 入 VERB v,動詞,行為,移動 _ 0 root _ Gloss=enter|SpaceAfter=No 3 虎 虎 NOUN n,名詞,主体,動物 _ 4 nmod _ Gloss=tiger|SpaceAfter=No 4 穴 穴 NOUN n,名詞,固定物,地形 _ 2 obj _ Gloss=cave|SpaceAfter=No 5 不 不 ADV v,副詞,否定,無界 _ 6 advmod _ Gloss=not|SpaceAfter=No 6 得 得 VERB v,動詞,行為,得失 _ 2 parataxis _ Gloss=get|SpaceAfter=No 7 虎 虎 NOUN n,名詞,主体,動物 _ 8 nmod _ Gloss=tiger|SpaceAfter=No 8 子 子 NOUN n,名詞,人,関係 _ 6 obj _ Gloss=child|SpaceAfter=No >>> t=d[0] >>> print(t.i+1,t.orth_,t.lemma_,t.pos_,t.tag_,t.head.i+1,t.dep_,t.whitespace_,t.norm_) 1 不 不 ADV v,副詞,否定,無界 2 advmod not ``` ## Installation for Linux Tar-ball is available for Linux, and is installed by default when you use `pip`: ```sh pip install udkanbun ``` ## Installation for Cygwin Make sure to get `gcc-g++` `python37-pip` `python37-devel` packages, and then: ```sh pip3.7 install udkanbun ``` Use `python3.7` command in [Cygwin](https://www.cygwin.com/install.html) instead of `python`. ## Installation for Jupyter Notebook (Google Colaboratory) ```py !pip install udkanbun ``` Try [notebook](https://colab.research.google.com/github/KoichiYasuoka/UD-Kanbun/blob/master/udkanbun.ipynb) for Google Colaboratory. ## Author Koichi Yasuoka (安岡孝一) ## References * Koichi Yasuoka: [Universal Dependencies Treebank of the Four Books in Classical Chinese](http://hdl.handle.net/2433/245217), DADH2019: 10th International Conference of Digital Archives and Digital Humanities (December 2019), pp.20-28. * 安岡孝一: [四書を学んだMeCab＋UDPipeはセンター試験の漢文を読めるのか](http://hdl.handle.net/2433/237383), 東洋学へのコンピュータ利用, 第30回研究セミナー (2019年3月8日), pp.3-110. * 安岡孝一: [漢文の依存文法解析と返り点の関係について](http://hdl.handle.net/2433/235609), 日本漢字学会第1回研究大会予稿集 (2018年12月1日), pp.33-48. %prep %autosetup -n udkanbun-3.3.7 %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-udkanbun -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 3.3.7-1 - Package Spec generated