Medical term normalization consists in mapping a piece of text to a large
number of output classes. Given the small size of the annotated datasets and
the extremely long tail distribution of the concepts, it is of utmost
importance to develop models that are capable to generalize to scarce or unseen
concepts. An important attribute of most target ontologies is their
hierarchical structure. In this paper we introduce a simple and effective
learning strategy that leverages such information to enhance the
generalizability of both discriminative and generative models. The evaluation
shows that the proposed strategy produces state-of-the-art performance on seen
concepts and consistent improvements on unseen ones, allowing also for
efficient zero-shot knowledge transfer across text typologies and datasets.

本论文介绍了一种简单而有效的学习策略，利用目标本体的层次结构来增强分类和生成模型的概括能力，该策略在已见概念上具有最先进的性能，在未见概念上也具有一致的改进，并可实现高效的零 - shot 知识转移。

针对医学术语标准化的长尾概念泛化

Generalizing over Long Tail Concepts for Medical Term Normalization

This paper proposes CODER: contrastive learning on knowledge graphs for
cross-lingual medical term representation. CODER is designed for medical term
normalization by providing close vector representations for different terms
that represent the same or similar medical concepts with cross-lingual support.
We train CODER via contrastive learning on a medical knowledge graph (KG) named
the Unified Medical Language System, where similarities are calculated
utilizing both terms and relation triplets from KG. Training with relations
injects medical knowledge into embeddings and aims to provide potentially
better machine learning features. We evaluate CODER in zero-shot term
normalization, semantic similarity, and relation classification benchmarks,
which show that CODERoutperforms various state-of-the-art biomedical word
embedding, concept embeddings, and contextual embeddings. Our codes and models
are available at this https URL

该研究提出了 CODER：针对跨语言医学术语表现的知识图谱对比学习。CODER 通过对 Unified Medical Language System 进行对比学习进行训练，通过利用词条和关系三元组之间的相似性将医学知识注入到嵌入中，并旨在提供潜在的更好的机器学习特性，评估结果表明 CODER 在零样本术语归一化，语义相似性和关系分类基准方面优于各种最先进的生物医学词嵌入，概念嵌入和上下文嵌入。