ChineseSemanticKB is a semantic knowledge base containing millions of common Chinese words, categorized into 12 types. Developed to address the need for practical, production-ready NLP resources, it offers lexicons like abstract, antonym, and synonym dictionaries. The project aims to bridge the gap between academic NLP research and industrial applications by providing readily usable vocabulary resources.
This project offers a comprehensive collection of Chinese semantic lexicons, addressing a gap in publicly available resources. The lexicons are derived from public text, manual curation, and automated extraction. It focuses on practical applications like event processing and opinion monitoring, designed with a modular structure suitable for integration into existing NLP pipelines.
- Abstract Relations: Includes abstract relationships between concepts for tasks like event abstraction and recommendation.
- Antonym Relations: Provides antonyms for sentence rewriting and data augmentation.
- Synonym Relations: Offers synonyms for query expansion and data augmentation to enhance NLP models.
- 简称 Relations: Facilitates sentence standardization and entity disambiguation through abbreviation mappings.
- Degree Adverbs: Covers adverbs of degree for sentiment intensity analysis and sentence generation.
- Negation Words: Includes negation words for sentiment calculation and other NLP tasks.
- Holiday Time Words: Specializes in identifying holiday and time-related terms for time expression recognition.
The project is actively maintained, with recent commits indicating ongoing development and updates. Documentation is available and the lexicons are readily downloadable. The project's focus on practical applications and community feedback suggests a commitment to long-term usability and improvement.
ChineseSemanticKB benefits NLP developers and researchers working with Chinese text. It simplifies the development of NLP applications such as sentiment analysis, question answering, and event extraction by providing pre-built semantic knowledge. The project offers a valuable resource for overcoming the challenge of building foundational vocabulary resources.