Abstract
Semantic indexing of biomedical literature is usually done at the level of MeSH descriptors, representing topics of interest for the biomedical community. Several related but distinct biomedical concepts are often grouped together in a single coarse-grained descriptor and are treated as a single topic for semantic indexing. This study proposes a new method for the automated refinement of subject annotations at the level of concepts, investigating deep learning approaches. Lacking labelled data for this task, our method relies on weak supervision based on concept occurrence in the abstract of an article. The proposed approach is evaluated on an extended large-scale retrospective scenario, taking advantage of concepts that eventually become MeSH descriptors, for which annotations become available in MEDLINE/PubMed. The results suggest that concept occurrence is a strong heuristic for automated subject annotation refinement and can be further enhanced when combined with dictionary-based heuristics. In addition, such heuristics can be useful as weak supervision for developing deep learning models that can achieve further improvement in some cases.
Abstract (translated)
生物医学文献的语义索引通常发生在 MeSH 描述符级别上,代表了生物医学社区感兴趣的主题。几个相关但不同的生物医学概念通常被组合在一起形成一个粗粒度的描述符,并被视为一个单一的主题进行语义索引。本研究提出了一种新方法,用于自动化改进主题注释的精度,研究深度学习方法。由于缺乏标记数据来完成这项工作,我们的方法依赖于基于概念在文章摘要中出现的弱监督。该方法在一项扩展的大跨度回顾场景中进行评估,利用最终成为 MeSH 描述符的概念,这些注释在 MEDLINE/PubMed 中可用。结果表明,概念出现是自动化主题注释精度的强大启发式,并可以与字典为基础的启发式相结合进行进一步增强。此外,这些启发式可以作为弱监督用于开发能够在某些情况下实现进一步改进的深度学习模型。
URL
https://arxiv.org/abs/2301.09350
