Abstract
The US National Library of Medicine (NLM) uses the Medical Subject Headings (MeSH) (see Note 1 ) to index almost all 24 million citations in MEDLINE, which greatly facilitates the application of biomedical information retrieval and text mining. Large-scale automatic MeSH indexing has two challenging aspects: the MeSH side and citation side. For the MeSH side, each citation is annotated by only 12 (on average) out of all 28,000 MeSH terms. For the citation side, all existing methods, including Medical Text Indexer (MTI) by NLM, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. To solve these two challenges, we developed the MeSHLabeler and DeepMeSH. By utilizing “learning to rank” (LTR) framework, MeSHLabeler integrates multiple types of information to solve the challenge in the MeSH side, while DeepMeSH integrates deep semantic representation to solve the challenge in the citation side. MeSHLabeler achieved the first place in both BioASQ2 and BioASQ3, and DeepMeSH achieved the first place in both BioASQ4 and BioASQ5 challenges. DeepMeSH is available at http://datamining-iip.fudan.edu.cn/deepmesh.
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Acknowledgments
This work has been partially supported by National Natural Science Foundation of China (Grant Nos: 61572139), MEXT KAKENHI #16H02868 and FiDiPro by Tekes.
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Peng, S., Mamitsuka, H., Zhu, S. (2018). MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing. In: Mamitsuka, H. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 1807. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8561-6_15
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DOI: https://doi.org/10.1007/978-1-4939-8561-6_15
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