PyCantonese: Cantonese Linguistics and Natural Language Processing in Python
Introduction
PyCantonese is a sophisticated Python library designed to facilitate Cantonese linguistics and natural language processing (NLP). Aimed at both academic researchers and developers, PyCantonese provides a collection of tools specifically tailored to handle various tasks in the realm of Cantonese language processing.
Key Features
The PyCantonese library offers an array of functionalities that cater to different aspects of language processing, including:
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Corpus Data Access and Search: This feature allows users to easily access and search through a rich corpus of Cantonese language data. It provides linguists and NLP practitioners the data needed to perform analyses and develop algorithms.
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Jyutping Romanization: PyCantonese includes tools for parsing and converting Cantonese text using Jyutping romanization, which aids in the standardization of Cantonese transcription.
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Cantonese Text Parsing: The library provides robust utilities for parsing Cantonese text, making it easier for applications to understand and process the language.
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Stop Words and Word Segmentation: Effective word segmentation and detection of stop words are crucial for text analysis, and PyCantonese delivers reliable methods to handle these tasks.
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Part-of-Speech Tagging: An essential feature for understanding linguistic structures, part-of-speech tagging in PyCantonese helps identify the grammatical roles of words within sentences.
Download and Installation
Users interested in leveraging PyCantonese can easily download and install the library using pip:
$ pip install --upgrade pycantonese
Further guidance on getting started with the library is available on the Quickstart page.
Consulting and Support
For academic and commercial entities seeking personalized support with PyCantonese, consulting services are available. Interested parties can reach out to Jackson L. Lee, the maintainer, for professional assistance.
Support from the community is appreciated, and users who find value in PyCantonese are encouraged to contribute by buying a coffee for the developer through BuyMeACoffee.
Additional Resources
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Source Code and Bug Tracker: PyCantonese’s source code is available on GitHub, where users can also report issues via the project's bug tracker.
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Social Media: Stay updated with PyCantonese developments through its Facebook and Twitter pages.
Licensing and Acknowledgments
The project is distributed under the MIT License, with the included HKCanCor dataset and rime-cantonese data shared under CC BY licenses. The PyCantonese logo, representing the Chinese character for Cantonese, was creatively designed by albino.snowman on Instagram.
Acknowledgment goes to various individuals and resources that have contributed to its development, showcasing a collective effort to enhance Cantonese linguistic tools.
Development and Changelog
Development enthusiasts can access the latest experimental features directly from the GitHub repository. Setting up a development environment is straightforward, and contributors can follow the detailed instructions provided to write tests or contribute to the documentation. Those interested in the evolution of PyCantonese can review the CHANGELOG.md for detailed updates on the project’s progression.
By integrating these diverse elements, PyCantonese establishes itself as an indispensable tool for those working with Cantonese linguistics and natural language processing, continually evolving with the contributions of its vibrant community.