Metadata-Version: 1.1
Name: nagisa
Version: 0.2.5
Summary: A Japanese tokenizer based on recurrent neural networks
Home-page: https://github.com/taishi-i/nagisa
Author: Taishi Ikeda
Author-email: taishi.ikeda.0323@gmail.com
License: MIT License
Download-URL: https://github.com/taishi-i/nagisa/archive/0.2.5.tar.gz
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        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+] <http://www.anlp.jp/proceedings/annual_meeting/2017/pdf_dir/B6-2.pdf>`__.
        - The POS-tagging model uses tag dictionary information
        `[Inoue+] <http://www.aclweb.org/anthology/K17-1042>`__.
        
        For more details refer to the following links. - The slides at PyCon JP
        2019 is available
        `here <https://speakerdeck.com/taishii/pycon-jp-2019>`__. - The article
        in Japanese is available
        `here <https://qiita.com/taishi-i/items/5b9275a606b392f7f58e>`__. - The
        documentation is available
        `here <https://nagisa.readthedocs.io/en/latest/?badge=latest>`__.
        
        Installation
        ============
        
        Python 2.7.x or 3.5+ is required. This tool uses
        `DyNet <https://github.com/clab/dynet>`__ (the Dynamic Neural Network
        Toolkit) to calcucate neural networks. You can install nagisa by using
        the following command.
        
        .. code:: bash
        
            pip install nagisa
        
        For Windows users, please run it with python 3.6+ (64bit).
        
        Basic usage
        ===========
        
        Sample of word segmentation and POS-tagging for Japanese.
        
        .. code:: python
        
            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.
        
        .. code:: python
        
            # 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.
        
        .. code:: python
        
            # 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
        
        Train a model
        =============
        
        Nagisa (v0.2.0+) 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`` tab ``tag``. Note that
        you put EOS between sentences. Refer to `sample
        datasets </nagisa/data/sample_datasets>`__ and `tutorial (Train a model
        for Universal
        Dependencies) <https://nagisa.readthedocs.io/en/latest/tutorial.html>`__.
        
        ::
        
            $ 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
        
        .. code:: python
        
            # 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
        
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Platform: Unix
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: Japanese
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: Unix
Classifier: Operating System :: Microsoft :: Windows
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Software Development :: Libraries :: Python Modules
