与 COVID-19 相关的文章数量不断增加，对 LitCovid 的手动管理和多标签主题分类提出了重大挑战。为此，本研究开发了一种新颖的多标签主题分类框架，该框架考虑了主题标签的相关性和不平衡性，同时增强了预训练模型的能力。借助该框架，本研究致力于回答以下问题：有关 COVID-19 的文章的全文、MeSH（医学主题标题）和生物实体是否比元数据（标题、摘要、关键词和期刊名称）？通过对我们的 BC7-LitCovid 语料库和 Hallmarks of Cancer 语料库的丰富版本进行的广泛实验，可以得出以下结论。我们的框架展示了卓越的性能和稳健性。有关 COVID-19 的科学出版物的元数据为多标签主题分类提供了有价值的信息。与生物实体相比，全文和 MeSH 可以进一步增强我们的多标签主题分类框架的性能，但改进的性能非常有限。数据库 URL：https://github.com/pzczxs/Enriched-BC7-LitCovid.© 作者 2024 年。由牛津大学出版社出版。

The ever-increasing volume of COVID-19-related articles presents a significant challenge for the manual curation and multilabel topic classification of LitCovid. For this purpose, a novel multilabel topic classification framework is developed in this study, which considers both the correlation and imbalance of topic labels, while empowering the pretrained model. With the help of this framework, this study devotes to answering the following question: Do full texts, MeSH (Medical Subject Heading), and biological entities of articles about COVID-19 encode more discriminative information than metadata (title, abstract, keyword, and journal name)? From extensive experiments on our enriched version of the BC7-LitCovid corpus and Hallmarks of Cancer corpus, the following conclusions can be drawn. Our framework demonstrates superior performance and robustness. The metadata of scientific publications about COVID-19 carries valuable information for multilabel topic classification. Compared to biological entities, full texts and MeSH can further enhance the performance of our framework for multilabel topic classification, but the improved performance is very limited. Database URL: https://github.com/pzczxs/Enriched-BC7-LitCovid.© The Author(s) 2024. Published by Oxford University Press.