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DeepMeSH: deep semantic representation for improving large-scale MeSH indexing

机译:DeepMeSH:深度语义表示,用于改善大规模MeSH索引

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Motivation: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well.
机译:动机:医学主题词索引(MeSH)索引是为引用分配一组MeSH主词,对于生物医学文本挖掘和信息检索中的许多重要任务至关重要。大规模MeSH索引具有两个挑战性方面:引文和MeSH。对于引用方面,所有现有方法,包括国家医学图书馆的医学文本索引器(MTI)和最新方法MeSHLabeler,都无法通过词袋处理文本,这些词无法捕获语义和上下文依赖信息。

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