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

Submission Type: Regular Long Paper

Submission Track: NLP Applications

Submission Track 2: NLP Applications

Keywords: text mining, medical text representation, imbalanced text classification

TL;DR: Explore medical text classification by learning text representations from data imbalance and scarcity perspective and align text representations with the disease label hierarchy.

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.

Submission Number: 1397