DeepMeSH: deep semantic representation for improving large-scale MeSH indexing.
DeepMeSH: deep semantic representation for improving large-scale MeSH indexing.
复制标题
DeepMeSH：用于改进大规模 MeSH 索引的深度语义表示
DOI：
10.1093/bioinformatics/btw294
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发表时间：
2016-06-15
期刊：
影响因子：
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通讯作者：
Zhu S
中科院分区：
文献类型：
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作者：
Peng S;You R;Wang H;Zhai C;Mamitsuka H;Zhu S
动机：医学主题词标引（MeSH）是将一组MeSH主词分配给引文，是生物医学文本挖掘和信息检索中的重要任务。大规模MeSH索引有两个挑战性的方面：引文方面和MeSH方面。在引文方面，现有的方法，包括美国国家医学图书馆的医学文本索引（MTI）和最先进的方法，MeSHLabeler，都是通过词袋来处理文本，不能很好地捕获语义和上下文相关的信息。方法：我们提出了DeepMeSH，它结合了深层语义信息，用于大规模MeSH索引。它解决了引文和MeSH方面的两个挑战。引用方的挑战是通过一种新的深层语义表示，D2 V-TFIDF，它连接稀疏和密集的语义表示。MeSH方面的挑战是通过使用MeSHLabeler的“学习排名”框架来解决的，该框架集成了从新的语义表示生成的各种类型的证据。结果如下：DeepMeSH在BioASQ 3挑战数据中获得了0.6323的Micro F-测量值，比MeSHLabeler的0.6218高2%，比MTI的0.5637高12%，引用次数为6000次。可用性和实施：该软件可应要求提供。联系方式：zhusf@fudan.edu.cn补充信息：补充数据可从生物信息学在线网站获得。
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. Methods: We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the ‘learning to rank’ framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation. Results: DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations. Availability and Implementation: The software is available upon request. Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.