SuPar-Kanbun
Tokenizer, POS-Tagger and Dependency-Parser for Classical Chinese Texts (漢文/文言文) with spaCy, Transformers and SuPar.
Basic usage
>>> import suparkanbun
>>> nlp=suparkanbun.load()
>>> doc=nlp("不入虎穴不得虎子")
>>> print(type(doc))
<class 'spacy.tokens.doc.Doc'>
>>> print(suparkanbun.to_conllu(doc))
# 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
>>> import deplacy
>>> deplacy.render(doc)
不 ADV <════╗ advmod
入 VERB ═══╗═╝═╗ ROOT
虎 NOUN <╗ ║ ║ nmod
穴 NOUN ═╝<╝ ║ obj
不 ADV <════╗ ║ advmod
得 VERB ═══╗═╝<╝ parataxis
虎 NOUN <╗ ║ nmod
子 NOUN ═╝<╝ obj
suparkanbun.load() has two options suparkanbun.load(BERT="roberta-classical-chinese-base-char",Danku=False). With the option Danku=True the pipeline tries to segment sentences automatically. Available BERT options are:
BERT="roberta-classical-chinese-base-char"utilizes roberta-classical-chinese-base-char (default)BERT="roberta-classical-chinese-large-char"utilizes roberta-classical-chinese-large-charBERT="guwenbert-base"utilizes GuwenBERT-baseBERT="guwenbert-large"utilizes GuwenBERT-largeBERT="sikubert"utilizes SikuBERTBERT="sikuroberta"utilizes SikuRoBERTa
Installation for Linux
pip3 install suparkanbun --user
Installation for Cygwin64
Make sure to get python37-devel python37-pip python37-cython python37-numpy python37-wheel gcc-g++ mingw64-x86_64-gcc-g++ git curl make cmake packages, and then:
curl -L https://raw.githubusercontent.com/KoichiYasuoka/CygTorch/master/installer/supar.sh | sh
pip3.7 install suparkanbun
Installation for Jupyter Notebook (Google Colaboratory)
!pip install suparkanbun
Try notebook for Google Colaboratory.
Author
Koichi Yasuoka (安岡孝一)
Reference
Koichi Yasuoka, Christian Wittern, Tomohiko Morioka, Takumi Ikeda, Naoki Yamazaki, Yoshihiro Nikaido, Shingo Suzuki, Shigeki Moro, Kazunori Fujita: Designing Universal Dependencies for Classical Chinese and Its Application, Journal of Information Processing Society of Japan, Vol.63, No.2 (February 2022), pp.355-363.
1 maintainer
Metadata
1 maintainer
Release files for suparkanbun 1.7.9
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