COVID-19流行病背景下的自监督语义索引方法
A Self-supervised Approach for Semantic Indexing in the Context of COVID-19 Pandemic
摘要：
疫情加快了COVID-19科学论文的发表速度。此外，专家手动为这些论文分配语义索引的过程在当前的健康危机中更加耗时和不堪重负。因此，迫切需要自动语义索引模型，可以有效地扩展到新引入的概念和快速发展的超聚焦相关文献的分布。在这项研究中，我们提出了一种新的语义索引方法的基础上，国家的最先进的自我监督表示学习和Transformer编码专门适用于流行病危机。我们提出了一个基于PubMed中发表和手动索引的COVID-19论文的新数据集的案例研究。我们的研究表明，我们的自监督模型优于BioASQ任务8a的最佳性能模型，平均微F1得分为0.1，LCA-F得分为0.08。我们的模型在检测补充概念方面也表现出优越的性能，当文献的重点急剧转向与大流行相关的特定概念时，这一点非常重要。我们的研究揭示了语义索引模型在大流行期间面临的主要挑战，即新的领域及其分布的急剧变化，并作为这种情况下的一种更好的选择，提出了一种基于在各种NLP任务中提高泛化和数据效率方面表现出良好性能的方法的模型。我们还表明，主要医学主题词（MeSH）和补充概念的联合索引提高了整体性能。
英文摘要：
The pandemic has accelerated the pace at which COVID-19 scientific papers are published. In addition, the process of manually assigning semantic indexes to these papers by experts is even more time-consuming and overwhelming in the current health crisis. Therefore, there is an urgent need for automatic semantic indexing models which can effectively scale-up to newly introduced concepts and rapidly evolving distributions of the hyperfocused related literature. In this research, we present a novel semantic indexing approach based on the state-of-the-art self-supervised representation learning and transformer encoding exclusively suitable for pandemic crises. We present a case study on a novel dataset that is based on COVID-19 papers published and manually indexed in PubMed. Our study shows that our self-supervised model outperforms the best performing models of BioASQ Task 8a by micro-F1 score of 0.1 and LCA-F score of 0.08 on average. Our model also shows superior performance on detecting the supplementary concepts which is quite important when the focus of the literature has drastically shifted towards specific concepts related to the pandemic. Our study sheds light on the main challenges confronting semantic indexing models during a pandemic, namely new domains and drastic changes of their distributions, and as a superior alternative for such situations, propose a model founded on approaches which have shown auspicious performance in improving generalization and data efficiency in various NLP tasks. We also show the joint indexing of major Medical Subject Headings (MeSH) and supplementary concepts improves the overall performance.
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