nagisa 0.3.0

A Japanese tokenizer based on recurrent neural networks

---

Nagisa is a python module for Japanese word segmentation/POS-tagging.

It is designed to be a simple and easy-to-use tool.

This tool has the following features.

• Based on recurrent neural networks.
• The word segmentation model uses character- and word-level features [池田+].
• The POS-tagging model uses tag dictionary information [Inoue+].

For more details refer to the following links.

• The documentation is available here.
• The article in Japanese is available here.
• The presentation slide at PyCon JP (2022) is available here.

Installation

You can install nagisa using pip:

pip install nagisa

Supported Platforms:

• 🐧 Linux: Python 3.6 - 3.14
• 🍎 macOS: Python 3.9 - 3.14
• 🪟 Windows: Python 3.9 - 3.14

Basic usage

Sample of word segmentation and POS-tagging for Japanese. The output tokens are normalized using Unicode NFKC normalization.

import nagisa

text = 'Pythonで簡単に使えるツールです'
words = nagisa.tagging(text)
print(words)
#=> Python/名詞 で/助詞 簡単/形状詞 に/助動詞 使える/動詞 ツール/名詞 です/助動詞

# Get a list of words
print(words.words)
#=> ['Python', 'で', '簡単', 'に', '使える', 'ツール', 'です']

# Get a list of POS-tags
print(words.postags)
#=> ['名詞', '助詞', '形状詞', '助動詞', '動詞', '名詞', '助動詞']

Post-processing functions

Filter and extarct words by the specific POS tags.

import nagisa

# Filter the words of the specific POS tags.
words = nagisa.filter(text, filter_postags=['助詞', '助動詞'])
print(words)
#=> Python/名詞 簡単/形状詞 使える/動詞 ツール/名詞

# Extarct only nouns.
words = nagisa.extract(text, extract_postags=['名詞'])
print(words)
#=> Python/名詞 ツール/名詞

# This is a list of available POS-tags in nagisa.
print(nagisa.tagger.postags)
#=> ['補助記号', '名詞', ... , 'URL']

Add the user dictionary in easy way.

import nagisa

# default
text = "3月に見た「3月のライオン」"
print(nagisa.tagging(text))
#=> 3/名詞 月/名詞 に/助詞 見/動詞 た/助動詞 「/補助記号 3/名詞 月/名詞 の/助詞 ライオン/名詞 」/補助記号

# If a word ("3月のライオン") is included in the single_word_list, it is recognized as a single word.
new_tagger = nagisa.Tagger(single_word_list=['3月のライオン'])
print(new_tagger.tagging(text))
#=> 3/名詞 月/名詞 に/助詞 見/動詞 た/助動詞 「/補助記号 3月のライオン/名詞 」/補助記号

Nagisa provides a built-in Japanese stopwords list.

import nagisa

# default
text = "日本語のストップワードを簡単に利用できます。"
tokens = nagisa.tagging(text)
print(tokens.words)
#=> ['日本', '語', 'の', 'ストップ', 'ワード', 'を', '簡単', 'に', '利用', 'でき', 'ます', '。']

# Filter out stopwords from the tokenized result
words = [word for word in tokens.words if word not in nagisa.stopwords]
print(words)
#=> ['日本', '語', 'ストップ', 'ワード', '簡単', '利用', '。']

Train a model

Nagisa provides a simple train method for a joint word segmentation and sequence labeling (e.g, POS-tagging, NER) model.

The format of the train/dev/test files is tsv. Each line is word and tag and one line is represented by word \t(tab) tag. Note that you put EOS between sentences. Refer to sample datasets and tutorial (Train a model for Universal Dependencies).

$ cat sample.train
唯一	NOUN
の	ADP
趣味	NOU
は	ADP
料理	NOUN
EOS
とても	ADV
おいしかっ	ADJ
た	AUX
です	AUX
。	PUNCT
EOS
ドル	NOUN
は	ADP
主要	ADJ
通貨	NOUN
EOS

import nagisa

# After finish training, save the three model files (*.vocabs, *.params, *.hp).
nagisa.fit(train_file="sample.train", dev_file="sample.dev", test_file="sample.test", model_name="sample")

# Build the tagger by loading the trained model files.
sample_tagger = nagisa.Tagger(vocabs='sample.vocabs', params='sample.params', hp='sample.hp')

text = "福岡・博多の観光情報"
words = sample_tagger.tagging(text)
print(words)
#> 福岡/PROPN ・/SYM 博多/PROPN の/ADP 観光/NOUN 情報/NOUN