论文标题
肯梅什：知识增强的端到端生物医学文本标签
KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling
论文作者
论文摘要
当前，医学主题标题（网格）是手动分配给发布并随后在PubMed数据库中记录的每篇生物医学文章，以促进检索相关信息。随着PubMed数据库的快速增长，大规模的生物医学文档索引变得越来越重要。网格索引是机器学习的一项艰巨的任务，因为它需要从一个非常大的高层组织收藏中为每篇文章分配多个标签。为了应对这一挑战，我们提出了肯梅什（Kenmesh），这是一种结合新文本功能的端到端模型，并且动态\ textbf {k} nowledge- \ \ textbf {en} hangage bask hanged bask hanged bask faste tocution tocution coasting将文档特征与网格标签层次结构和日记帐相关性集成到索引网格项中。实验结果表明，所提出的方法在许多衡量标准上实现了最先进的绩效。
Currently, Medical Subject Headings (MeSH) are manually assigned to every biomedical article published and subsequently recorded in the PubMed database to facilitate retrieving relevant information. With the rapid growth of the PubMed database, large-scale biomedical document indexing becomes increasingly important. MeSH indexing is a challenging task for machine learning, as it needs to assign multiple labels to each article from an extremely large hierachically organized collection. To address this challenge, we propose KenMeSH, an end-to-end model that combines new text features and a dynamic \textbf{K}nowledge-\textbf{en}hanced mask attention that integrates document features with MeSH label hierarchy and journal correlation features to index MeSH terms. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures.
