Jieba – Chinese Word Segmentation
- Project:
- Language: C++ / Python
Overview
Jieba is a high-performance Chinese word segmentation library designed for NLP preprocessing, search indexing, and text analytics. It supports dictionary-based, probabilistic, and HMM-driven segmentation, allowing accurate tokenization for both Simplified and Traditional Chinese.
Jieba is primarily used in offline or online NLP pipelines, including search engines, recommendation systems, and content analysis tools. It is optimized for speed and accuracy over large-scale corpora while maintaining a small memory footprint.
Key Features
- Multiple segmentation modes: Full mode, precise mode, and search engine mode.
- Custom dictionary support: Users can add domain-specific words and phrases.
- HMM-based unknown word recognition: Handles OOV (out-of-vocabulary) words using probabilistic modeling.
- Unicode support: Works with UTF-8 Chinese text, including mixed Simplified and Traditional input.
- Extensible and easy to integrate: Provides C++ API, Python bindings, and command-line tools.
Typical Use Cases
- Preprocessing Chinese text for NLP pipelines.
- Tokenizing content for search indexing and query segmentation.
- Named entity recognition (NER) preprocessing.
- Real-time or batch text analytics for recommendation or content moderation.
Industrial Fit
| Requirement | Jieba Support |
|---|---|
| Accurate Chinese word segmentation | ✔️ |
| Custom dictionary integration | ✔️ |
| Unknown word handling (HMM) | ✔️ |
| High-volume text processing | ✔️ |
| Simplified / Traditional Chinese | ✔️ |