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
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
Jiagu深度学习自然语言处理工具 知识图谱关系抽取 中文分词 词性标注 命名实体识别 情感分析 新词发现 关键词 文本摘要 文本聚类
The main features of ownthink/jiagu are: Knowledge Graphs, Chinese NLP Toolkits.
Open-source alternatives to ownthink/jiagu include: dongrixinyu/jionlp — JioNLP is a Chinese natural language processing toolkit designed for cleaning, normalizing, and extracting structured… baidu/lac — LAC is a Chinese lexical analysis engine and toolkit designed for joint word segmentation, part-of-speech tagging, and… crownpku/chinese-annotator. awslabs/dgl-ke — High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings. dstlry/dstlr — scalable knowledge graph construction from unstructured text. accenture/ampligraph — Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org.