COVID-19大流行背景下生物医学文章的Zero-Shot和Few-Shot分类
Zero-Shot and Few-Shot Classification of Biomedical Articles in Context of the COVID-19 Pandemic
摘要：
MeSH（Medical Subject Headings）是美国国家医学图书馆创建的大型主题词表，用于生物医学领域出版物的细粒度索引。在2019冠状病毒病疫情的背景下，MeSH描述符出现在与相应主题相关的文章中。Zero-shot分类是一个足够的响应，及时标记的论文流与MeSH类别。在这项工作中，我们假设，丰富的语义信息，在MeSH有可能改善BioBERT表示，使它们更适合于zero-shot/Few-Shot任务。我们将问题定义为确定MeSH术语定义，与论文摘要连接是否是有效实例，并利用多任务学习来诱导MeSH层次结构，这要归功于seq 2seq任务。结果建立了MedLine和LitCovid数据集的基线，探测表明，所得表示传达了MeSH中存在的层次关系。
英文摘要：
MeSH (Medical Subject Headings) is a large thesaurus created by the National Library of Medicine and used for fine-grained indexing of publications in the biomedical domain. In the context of the COVID-19 pandemic, MeSH descriptors have emerged in relation to articles published on the corresponding topic. Zero-shot classification is an adequate response for timely labeling of the stream of papers with MeSH categories. In this work, we hypothesise that rich semantic information available in MeSH has potential to improve BioBERT representations and make them more suitable for zero-shot/few-shot tasks. We frame the problem as determining if MeSH term definitions, concatenated with paper abstracts are valid instances or not, and leverage multi-task learning to induce the MeSH hierarchy in the representations thanks to a seq2seq task. Results establish a baseline on the MedLine and LitCovid datasets, and probing shows that the resulting representations convey the hierarchical relations present in MeSH.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
