Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium 2012
DOI: 10.1145/2110363.2110450
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Cited by 15 publications
(2 citation statements)
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“…Topic-dependent results were also found in MeSH indexing [47,48]. In our study, we also noted that classification performance using references by section was dependent on the topic.…”
Section: Discussionsupporting
confidence: 79%
“…Topic-dependent results were also found in MeSH indexing [47,48]. In our study, we also noted that classification performance using references by section was dependent on the topic.…”
Section: Discussionsupporting
confidence: 79%
“…Many studies have been carried out to tackle the challenging problem of automatic MeSH indexing based on different principles, such as k -nearest neighbor (KNN) ( Trieschnigg et al , 2009 ), Naive Bayes ( Jimeno-Yepes et al , 2012b ), support vector machine (SVM) ( Jimeno-Yepes et al , 2012a ), Learning to Rank (LTR) ( Huang et al , 2011a ; Liu et al , 2015 ; Mao and Lu, 2013 ), deep learning ( Jimeno-Yepes et al , 2014 ; Rios and Kavuluru, 2015 ) and multi-label learning ( Liu et al , 2015 ; Tsoumakas et al , 2013 ). MeSHLabeler is a state-of-the-art automatic MeSH indexing algorithm, which won the first place in the large-scale MeSH indexing task of both BioASQ2 and BioASQ3 competition ( http://bioasq.org ) ( Liu et al , 2015 ; Tsatsaronis et al , 2015 ).…”
Section: Introductionmentioning
confidence: 99%