Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification
Jiahuan Yan, Haojun Gao, Zhang Kai, Weize Liu, Danny Chen, Jian Wu, Jintai Chen
Abstract
Deep learning approaches exhibit promising performances on various text tasks. However, they are still struggling on medical text classification since samples are often extremely imbalanced and scarce. Different from existing mainstream approaches that focus on supplementary semantics with external medical information, this paper aims to rethink the data challenges in medical texts and present a novel framework-agnostic algorithm called Text2Tree that only utilizes internal label hierarchy in training deep learning models. We embed the ICD code tree structure of labels into cascade attention modules for learning hierarchy-aware label representations. Two new learning schemes, Similarity Surrogate Learning (SSL) and Dissimilarity Mixup Learning (DML), are devised to boost text classification by reusing and distinguishing samples of other labels following the label representation hierarchy, respectively. Experiments on authoritative public datasets and real-world medical records show that our approach stably achieves superior performances over classical and advanced imbalanced classification methods. Our code is available at https://github.com/jyansir/Text2Tree.- Anthology ID:
- 2023.findings-emnlp.517
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7705–7720
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.517
- DOI:
- 10.18653/v1/2023.findings-emnlp.517
- Cite (ACL):
- Jiahuan Yan, Haojun Gao, Zhang Kai, Weize Liu, Danny Chen, Jian Wu, and Jintai Chen. 2023. Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7705–7720, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification (Yan et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.findings-emnlp.517.pdf