Few-shot HTC

Few-shot Hierarchical Text Classification (Few-shot HTC) is an advanced technique in the field of natural language processing aimed at achieving efficient and accurate hierarchical text classification with a limited number of samples. Its core objective is to use a small amount of labeled data to build models that can identify and classify multi-level textual information, thereby reducing the cost of data annotation and enhancing the model's generalization and adaptability. Few-shot HTC has significant application value in scenarios such as information retrieval, document management, and knowledge graph construction, effectively improving the intelligence level of text processing.