Sememe Knowledge

Sememe knowledge, focusing on the minimum units of meaning in language, aims to improve natural language processing (NLP) tasks by incorporating semantic information beyond word-level representations. Current research emphasizes leveraging sememe knowledge bases, often integrated with deep learning models like transformers and recurrent neural networks, to enhance performance in areas such as relation extraction, speech recognition, and semantic consistency recognition, particularly for handling long-tail data and cross-lingual applications. This work is significant because it addresses limitations of purely data-driven approaches by enriching NLP models with explicit semantic knowledge, leading to more robust and accurate systems for various applications.