Vietnamese NLP Toolkit
Project description
========================================
Under The Sea - Vietnamese NLP Toolkit
========================================
.. image:: https://img.shields.io/pypi/v/underthesea.svg
:target: https://pypi.python.org/pypi/underthesea
.. image:: https://img.shields.io/travis/magizbox/underthesea.svg
:target: https://travis-ci.org/magizbox/underthesea
.. image:: https://readthedocs.com/projects/magizbox-underthesea/badge/?version=latest
:target: https://magizbox-underthesea.readthedocs-hosted.com/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://pyup.io/repos/github/magizbox/underthesea/shield.svg
:target: https://pyup.io/repos/github/magizbox/underthesea/
:alt: Updates
|
.. image:: https://raw.githubusercontent.com/magizbox/underthesea/master/logo.jpg
:target: https://raw.githubusercontent.com/magizbox/underthesea/master/logo.jpg
**underthesea** is a suite of open source Python modules, data sets and tutorials supporting research and development in Vietnamese Natural Language Processing.
* Free software: GNU General Public License v3
* Documentation: `https://underthesea.readthedocs.io <https://magizbox-underthesea.readthedocs-hosted.com/en/latest/>`_
Installation
----------------------------------------
To install underthesea, simply:
.. code-block:: bash
$ pip install underthesea
✨🍰✨
Satisfaction, guaranteed.
Usage
----------------------------------------
* `1. Corpus <#1-corpus>`_
* `2. Word Segmentation <#2-word-segmentation>`_
* `3. POS Tagging <#3-pos-tagging>`_
****************************************
1. Corpus
****************************************
.. image:: https://img.shields.io/badge/documents-18k-red.svg
:target: #
.. image:: https://img.shields.io/badge/words-74k-red.svg
:target: #
Collection of Vietnamese corpus
* `Vietnamese Dictionary (74k words) <https://github.com/magizbox/underthesea/tree/master/underthesea/corpus/data>`_
* `Vietnamese News Corpus (10k documents) <https://github.com/magizbox/corpus.vinews>`_
* `Vietnamese Wikipedia Corpus (8k documents) <https://github.com/magizbox/corpus.viwiki>`_
****************************************
2. Word Segmentation
****************************************
.. image:: https://img.shields.io/badge/F1-97%25-red.svg
:target: https://github.com/magizbox/underthesea.word_sent
.. image:: https://img.shields.io/badge/%E2%98%85-can%20beat%20it%3F-blue.svg
:target: https://github.com/magizbox/underthesea.word_sent
Vietnamese Word Segmentation using Conditional Random Fields
* `Word Segmentation API <https://magizbox-underthesea.readthedocs-hosted.com/en/latest/api.html#word-sent-package>`_
* `Word Segmentation Experiences <https://github.com/magizbox/underthesea.word_sent>`_
.. code-block:: python
>>> from underthesea import word_sent
>>> sentence = u"Chúng ta thường nói đến Rau sạch, Rau an toàn để phân biệt với các rau bình thường bán ngoài chợ."
>>> word_sent(sentence)
[u"Chúng ta", u"thường", u"nói", u"đến", u"Rau sạch", u",", u"Rau", u"an toàn", u"để", u"phân biệt", u"với",
u"các", u"rau", u"bình thường", u"bán", u"ngoài", u"chợ", u"."]
>>> word_sent(sentence, format="text")
u'Chúng_ta thường nói đến Rau_sạch , Rau an_toàn để phân_biệt với các rau bình_thường bán ngoài chợ .'
****************************************
3. POS Tagging
****************************************
.. image:: https://img.shields.io/badge/accuracy-92.3%25-red.svg
:target: https://github.com/magizbox/underthesea.pos_tag
.. image:: https://img.shields.io/badge/%E2%98%85-can%20beat%20it%3F-blue.svg
:target: https://github.com/magizbox/underthesea.pos_tag
Vietnamese Part of Speech Tagging using Conditional Random Fields
* `POS Tagging API <https://magizbox-underthesea.readthedocs-hosted.com/en/latest/api.html#pos-tag-package>`_
* `Pos Tagging Experiences <https://github.com/magizbox/underthesea.pos_tag>`_
.. code-block:: python
# -*- coding: utf-8 -*-
>>> from underthesea import pos_tag
>>> text = u"Chợ thịt chó nổi tiếng ở TP Hồ Chí Minh bị truy quét"
>>> pos_tag(text)
[(u'Chợ', 'N'),
(u'thịt', 'N'),
(u'chó', 'N'),
(u'nổi tiếng', 'A'),
(u'ở', 'E'),
(u'TP HCM', 'Np'),
(u'bị', 'V'),
(u'truy quét', 'V')]
Up Coming Features
----------------------------------------
* Word Representation (`Word Representation Experiences <https://github.com/magizbox/underthesea.word_representation>`_)
* Chunking (Experiences)
* Dependency Parsing (Experiences)
* Named Entity Recognition
* Sentiment Analysis
Contributing
----------------------------------------
Do you want to contribute with underthesea development? Great! Please read more details at `CONTRIBUTING.rst. <https://github.com/magizbox/underthesea/blob/master/CONTRIBUTING.rst>`_
========================================
History
========================================
1.0.18 (2017-05-24)
----------------------------------------
* Fix word_sent method
* Enhance performance
* Add word_sent package
1.0.9 (2017-03-07)
----------------------------------------
* Add Corpus class
* Add Transformer classes
* Integrated with dictionary of Ho Ngoc Duc
* Add travis-CI
* Auto build with PyPI
1.0.0 (2017-03-01)
----------------------------------------
* First release on PyPI.
* First release on Readthedocs
Under The Sea - Vietnamese NLP Toolkit
========================================
.. image:: https://img.shields.io/pypi/v/underthesea.svg
:target: https://pypi.python.org/pypi/underthesea
.. image:: https://img.shields.io/travis/magizbox/underthesea.svg
:target: https://travis-ci.org/magizbox/underthesea
.. image:: https://readthedocs.com/projects/magizbox-underthesea/badge/?version=latest
:target: https://magizbox-underthesea.readthedocs-hosted.com/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://pyup.io/repos/github/magizbox/underthesea/shield.svg
:target: https://pyup.io/repos/github/magizbox/underthesea/
:alt: Updates
|
.. image:: https://raw.githubusercontent.com/magizbox/underthesea/master/logo.jpg
:target: https://raw.githubusercontent.com/magizbox/underthesea/master/logo.jpg
**underthesea** is a suite of open source Python modules, data sets and tutorials supporting research and development in Vietnamese Natural Language Processing.
* Free software: GNU General Public License v3
* Documentation: `https://underthesea.readthedocs.io <https://magizbox-underthesea.readthedocs-hosted.com/en/latest/>`_
Installation
----------------------------------------
To install underthesea, simply:
.. code-block:: bash
$ pip install underthesea
✨🍰✨
Satisfaction, guaranteed.
Usage
----------------------------------------
* `1. Corpus <#1-corpus>`_
* `2. Word Segmentation <#2-word-segmentation>`_
* `3. POS Tagging <#3-pos-tagging>`_
****************************************
1. Corpus
****************************************
.. image:: https://img.shields.io/badge/documents-18k-red.svg
:target: #
.. image:: https://img.shields.io/badge/words-74k-red.svg
:target: #
Collection of Vietnamese corpus
* `Vietnamese Dictionary (74k words) <https://github.com/magizbox/underthesea/tree/master/underthesea/corpus/data>`_
* `Vietnamese News Corpus (10k documents) <https://github.com/magizbox/corpus.vinews>`_
* `Vietnamese Wikipedia Corpus (8k documents) <https://github.com/magizbox/corpus.viwiki>`_
****************************************
2. Word Segmentation
****************************************
.. image:: https://img.shields.io/badge/F1-97%25-red.svg
:target: https://github.com/magizbox/underthesea.word_sent
.. image:: https://img.shields.io/badge/%E2%98%85-can%20beat%20it%3F-blue.svg
:target: https://github.com/magizbox/underthesea.word_sent
Vietnamese Word Segmentation using Conditional Random Fields
* `Word Segmentation API <https://magizbox-underthesea.readthedocs-hosted.com/en/latest/api.html#word-sent-package>`_
* `Word Segmentation Experiences <https://github.com/magizbox/underthesea.word_sent>`_
.. code-block:: python
>>> from underthesea import word_sent
>>> sentence = u"Chúng ta thường nói đến Rau sạch, Rau an toàn để phân biệt với các rau bình thường bán ngoài chợ."
>>> word_sent(sentence)
[u"Chúng ta", u"thường", u"nói", u"đến", u"Rau sạch", u",", u"Rau", u"an toàn", u"để", u"phân biệt", u"với",
u"các", u"rau", u"bình thường", u"bán", u"ngoài", u"chợ", u"."]
>>> word_sent(sentence, format="text")
u'Chúng_ta thường nói đến Rau_sạch , Rau an_toàn để phân_biệt với các rau bình_thường bán ngoài chợ .'
****************************************
3. POS Tagging
****************************************
.. image:: https://img.shields.io/badge/accuracy-92.3%25-red.svg
:target: https://github.com/magizbox/underthesea.pos_tag
.. image:: https://img.shields.io/badge/%E2%98%85-can%20beat%20it%3F-blue.svg
:target: https://github.com/magizbox/underthesea.pos_tag
Vietnamese Part of Speech Tagging using Conditional Random Fields
* `POS Tagging API <https://magizbox-underthesea.readthedocs-hosted.com/en/latest/api.html#pos-tag-package>`_
* `Pos Tagging Experiences <https://github.com/magizbox/underthesea.pos_tag>`_
.. code-block:: python
# -*- coding: utf-8 -*-
>>> from underthesea import pos_tag
>>> text = u"Chợ thịt chó nổi tiếng ở TP Hồ Chí Minh bị truy quét"
>>> pos_tag(text)
[(u'Chợ', 'N'),
(u'thịt', 'N'),
(u'chó', 'N'),
(u'nổi tiếng', 'A'),
(u'ở', 'E'),
(u'TP HCM', 'Np'),
(u'bị', 'V'),
(u'truy quét', 'V')]
Up Coming Features
----------------------------------------
* Word Representation (`Word Representation Experiences <https://github.com/magizbox/underthesea.word_representation>`_)
* Chunking (Experiences)
* Dependency Parsing (Experiences)
* Named Entity Recognition
* Sentiment Analysis
Contributing
----------------------------------------
Do you want to contribute with underthesea development? Great! Please read more details at `CONTRIBUTING.rst. <https://github.com/magizbox/underthesea/blob/master/CONTRIBUTING.rst>`_
========================================
History
========================================
1.0.18 (2017-05-24)
----------------------------------------
* Fix word_sent method
* Enhance performance
* Add word_sent package
1.0.9 (2017-03-07)
----------------------------------------
* Add Corpus class
* Add Transformer classes
* Integrated with dictionary of Ho Ngoc Duc
* Add travis-CI
* Auto build with PyPI
1.0.0 (2017-03-01)
----------------------------------------
* First release on PyPI.
* First release on Readthedocs
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