Journal article
MeSHProbeNet-P: Improving Large-scale MeSH Indexing with Personalizable MeSH Probes
ACM transactions on knowledge discovery from data, Vol.15(1), pp.1-14
01/01/2021
DOI: 10.1145/3421713
Abstract
Indexing biomedical research articles with Medical Subject Headings (MeSH) can greatly facilitate biomedical research and information retrieval. Currently MeSH indexing is performed by human experts. To alleviate the time consumption and monetary cost caused by manual indexing, many automatic MeSH indexing models have been developed, such as MeSHProbeNet, DeepMeSH, and NLM's official model Medical Text Indexer. In this article, we propose an end-to-end framework, MeSHProbeNet-P, which extends MeSHProbeNet with personalizable MeSH probes. In MeSHProbeNet-P, each MeSH probe carries certain aspects of biomedical knowledge and extracts related information from input articles. MeSHProbeNet-P is able to automatically personalize its MeSH probes for different input articles to ensure that the current MeSH probes best fit the current input article and the most informative features can be extracted from the article. We demonstrate the effectiveness of MeSHProbeNet-P in a real-world large-scale MeSH indexing challenge. MeSHProbeNet-P won the first place in the first batch of Task A in the 2019 BioASQ challenge. The result on the first test set of the challenge is reported in this article. We also provide ablation studies to show the advantages of personalizable MeSH probes.
Details
- Title: Subtitle
- MeSHProbeNet-P: Improving Large-scale MeSH Indexing with Personalizable MeSH Probes
- Creators
- Guangxu Xun - University of VirginiaKishlay Jha - University of VirginiaAidong Zhang - University of Virginia
- Resource Type
- Journal article
- Publication Details
- ACM transactions on knowledge discovery from data, Vol.15(1), pp.1-14
- Publisher
- Assoc Computing Machinery
- DOI
- 10.1145/3421713
- ISSN
- 1556-4681
- eISSN
- 1556-472X
- Number of pages
- 14
- Grant note
- IIS-1924928; IIS-1938167; OAC-1934600 / US National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2021
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984294927602771
Metrics
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