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Installation for Jupyter Notebook (Google Colaboratory) ! pip install udkanbun Use python3.7 command in Cygwin instead of python. Make sure to get gcc-g++ python37-pip python37-devel packages, and then: pip3.7 install udkanbun Tar-ball is available for Linux, and is installed by default when you use pip: pip install udkanbun norm_ ) 1 不 不 ADV v, 副詞, 否定, 無界 2 advmod not Installation for Linux 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 > print ( t. > import udkanbun.spacy > lzh = udkanbun. If you have already installed spaCy 2.1.0 or later, you can use UD-Kanbun via spaCy Language pipeline. You can simply use udkanbun on the command line: echo 不入虎穴不得虎子 | udkanbun to_tree(kaeriten=True,Japanese=True) is convenient for Japanese users.
_tree() has an option to_tree(BoxDrawingWidth=2) for old terminals, whose Box Drawing characters are "fullwidth". With the option Danku=True the pipeline tries to segment sentences automatically. With the option MeCab=False the pipeline uses UDPipe for all through the processing. By default, the UD-Kanbun pipeline uses MeCab for tokenizer and POS-tagger, then uses UDPipe for dependency-parser. Udkanbun.load() has three options udkanbun.load(MeCab=True,Danku=False). misc ) 1 不 不 ADV v, 副詞, 否定, 無界 Polarity = Neg 2 advmod _ Gloss = not | SpaceAfter = No > print ( s. 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 > print ( t. Basic usage > import udkanbun > lzh = udkanbun. Tokenizer, POS-Tagger, and Dependency-Parser for Classical Chinese Texts (漢文/文言文), working on Universal Dependencies.
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