Xuhong Zhang, Qian Wu, Yuke Li, Ke Xiong, Wangjie Gan
We lifted 10 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| happywinder/SCHK-HTC | canonical | 7 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| flat_contrastive_loss_func | Ran | happywinder/SCHK-HTC/models/loss.py pointer only (licence: NONE) · get_code("d0609b22f6776b04") |
| get_umls_neighbors | Ran | happywinder/SCHK-HTC/KG/add_neighbor.py pointer only (licence: NONE) · get_code("b9730a1f561ca16e") |
| load_edge_index | Ran | happywinder/SCHK-HTC/KG/deepwalk.py pointer only (licence: NONE) · get_code("0c201bdd1b74c3ac") |
| load_kg_data | Ran | happywinder/SCHK-HTC/KG/create_embedding.py pointer only (licence: NONE) · get_code("c290744aae4d1f42") |
| load_umls_relations | Ran | happywinder/SCHK-HTC/KG/add_neighbor.py pointer only (licence: NONE) · get_code("242fbb2b44aea26a") |
| query_conceptnet | Ran | happywinder/SCHK-HTC/query.py pointer only (licence: NONE) · get_code("0cc82608dcded677") |
| sim | Ran | happywinder/SCHK-HTC/models/loss.py pointer only (licence: NONE) · get_code("7f715d5aa1c04819") |
| constraint_multi_depth_loss_func_inverse | Not yet run | happywinder/SCHK-HTC/models/loss.py pointer only (licence: NONE) · get_code("77936f8eb7dd305f") |
| get_hybrid_entity_info | Not yet run | happywinder/SCHK-HTC/KG/linker.py pointer only (licence: NONE) · get_code("a7954e23a612ae67") |
| train_with_deepwalk | Not yet run | happywinder/SCHK-HTC/KG/deepwalk.py pointer only (licence: NONE) · get_code("0c34c5f611850333") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Few-shot Hierarchical Text Classification (few-shot HTC) is a challenging task that involves mapping texts to a predefined treestructured label hierarchy under data-scarce conditions. While current approaches utilize structural constraints from the label hierarchy to maintain parent-child prediction consistency, they face a critical bottleneck, the difficulty in distinguishing semantically similar sibling classes due to insufficient domain knowledge. We introduce an innovative method named Sibling Contrastive Learning with Hierarchical Knowledge-aware Prompt Tuning for fewshot HTC tasks (SCHK-HTC). Our work enhances the model's perception of subtle differences between sibling classes at deeper levels, rather than just enforcing hierarchical rules. Specifically, we propose a novel framework featuring two core components: a hierarchical knowledge extraction module and a sibling contrastive learning mechanism. This design guides model to encode discriminative features at each hierarchy level, thus improving the separability of confusable classes. Our approach achieves superior performance across three benchmark datasets, surpassing existing state-of-the-art methods in most cases. Our code is available at https://github.com/happywinder/SCHK-HTC.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2604.15998")
get_code_for_paper("2604.15998")
have("2604.15998")
Connect an agent — have() is free.