PyCantonese: Cantonese Linguistics and NLP in Python

PyCantonese is a Python library for Cantonese linguistics and natural language processing (NLP). Currently implemented features (more to come!):

  • Accessing and searching corpus data

  • Parsing and conversion tools for Jyutping romanization

  • Parsing Cantonese text

  • Stop words

  • Word segmentation

  • Part-of-speech tagging

Download and Install

To download and install the stable, most recent version:

$ pip install --upgrade pycantonese

Ready for more? Check out the Quickstart page.


If your team would like professional assistance in using PyCantonese, freelance consulting and training services are available for both academic and commercial groups. Please email Jackson L. Lee.


If you have found PyCantonese useful and would like to offer support, buying me a coffee would go a long way!

How to Cite

PyCantonese is authored and maintained by Jackson L. Lee.

Lee, Jackson L., Litong Chen, Charles Lam, Chaak Ming Lau, and Tsz-Him Tsui. 2022. PyCantonese: Cantonese Linguistics and NLP in Python. Proceedings of the 13th Language Resources and Evaluation Conference.

   title = "PyCantonese: Cantonese Linguistics and NLP in Python",
   author = "Lee, Jackson L.  and
      Chen, Litong  and
      Lam, Charles  and
      Lau, Chaak Ming  and
      Tsui, Tsz-Him",
   booktitle = "Proceedings of The 13th Language Resources and Evaluation Conference",
   month = june,
   year = "2022",
   publisher = "European Language Resources Association",
   language = "English",


MIT License. Please see LICENSE.txt in the GitHub source code for details.

The HKCanCor dataset included in PyCantonese is substantially modified from its source in terms of format. The original dataset has a CC BY license. Please see pycantonese/data/hkcancor/ in the GitHub source code for details.

The rime-cantonese data (release 2021.05.16) is incorporated into PyCantonese for word segmentation and characters-to-Jyutping conversion. This data has a CC BY 4.0 license. Please see pycantonese/data/rime_cantonese/ in the GitHub source code for details.


Wonderful resources with a permissive license that have been incorporated into PyCantonese:

  • HKCanCor

  • rime-cantonese

Individuals who have contributed feedback, bug reports, etc. (in alphabetical order of last names):

  • @cathug

  • Jenny Chim

  • @g-traveller

  • Rachel Han

  • Ryan Lai

  • Hill Ma

  • @richielo

  • @rylanchiu

  • Stephan Stiller

  • Robin Yuen

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