How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
The main features of crownpku/chinese-annotator are: Chinese NLP Toolkits.
Projects with overlapping indexed features include: dongrixinyu/jionlp — JioNLP is a Chinese natural language processing toolkit designed for cleaning, normalizing, and extracting structured… fastnlp/fastnlp. fudannlp/fnlp — FudanNLP (FNLP). hankcs/hanlp — HanLP is a natural language processing library and deep learning framework specifically optimized for the Chinese… hit-scir/ltp — This is a Chinese natural language processing toolkit providing a suite of tools for word segmentation, part-of-speech… baidu/lac — LAC is a Chinese lexical analysis engine and toolkit designed for joint word segmentation, part-of-speech tagging, and…
JioNLP is a Chinese natural language processing toolkit designed for cleaning, normalizing, and extracting structured information from unstructured text. It functions as a linguistic analyzer for Chinese characters and a rule-based named entity extractor, providing a specialized system for sentiment scoring and synthetic data generation for machine learning workflows. The project features a lexicon-based sentiment analysis engine that computes numerical emotional tone scores and a data augmentation library that uses back-translation and synonym replacement to expand training datasets. It incl
LAC is a Chinese lexical analysis engine and toolkit designed for joint word segmentation, part-of-speech tagging, and named entity recognition. It functions as a high-performance system that identifies word boundaries and grammatical categories using trained machine learning models. The project features a lightweight, compiled native runtime that enables on-device natural language processing and embedding into mobile applications. It includes model compression and conversion to optimize for resource-constrained environments and supports multi-threaded parallel execution to increase throughpu
