CanCLID: Cantonese Computational Linguistics Infrastructure
CanCLID: Building Cantonese Language Technology Infrastructure
The Cantonese Computational Linguistics Infrastructure Development Workgroup (CanCLID) represents a collaborative effort to build comprehensive computational resources for the Cantonese language.
Major Contributions
Corpus Development
- Large-scale Data Collection: Collected and cleaned extensive Guangzhou Cantonese corpus
- Quality Assurance: Implemented rigorous data cleaning and validation procedures
- Accessibility: Made corpus resources available for research community
Machine Learning Applications
- Language Classification: Developed and trained Cantonese/Mandarin classification models
- Model Optimization: Fine-tuned models for accuracy in distinguishing Sinitic languages
- Evaluation Metrics: Established benchmarks for Cantonese NLP tasks
Mozilla Common Voice Localization
- UI Translation: Complete translation of Mozilla Common Voice interface into Cantonese
- Corpus Curation: Systematic collection and organization of Cantonese speech data
- Audio Recording: Participation in community recording efforts
- Quality Validation: Audio review and validation for dataset quality
Input Method Development
- Algorithm Optimization: Enhanced IME algorithms specifically for Cantonese text input
- Performance Improvement: Focused on speed and accuracy for real-world usage
- Integration Testing: Ensured compatibility with existing language technology stack
Technical Expertise
- Python Development: Extensive use of pandas for data processing and analysis
- Machine Learning: Experience with classification algorithms and model training
- Corpus Linguistics: Large-scale text processing and linguistic annotation
- Community Development: Collaborative open-source project management
Impact & Recognition
CanCLID’s work has significantly advanced the state of Cantonese language technology, providing essential infrastructure for researchers, developers, and the Cantonese-speaking community.
Project Status: Active contributor (2020 - Present)
Organization: Cantonese Computational Linguistics Infrastructure Development Workgroup
Repository: github.com/CanCLID