Beyond MeSH: Fine-grained semantic indexing of biomedical literature based on weak supervision
作者：
Highlights：
• Semantic indexing with MeSH descriptors may aggregate several distinct concepts.
• Concept-occurrence is a good heuristic for fine-grained semantic indexing.
• Models trained with concept-occurrence as weak supervision can achieve good accuracy.
• Lexical and semantic features combined can lead to improved predictive performance.
• Under-sampling the major class in training data, can also lead to further improvement.
摘要
•Semantic indexing with MeSH descriptors may aggregate several distinct concepts.•Concept-occurrence is a good heuristic for fine-grained semantic indexing.•Models trained with concept-occurrence as weak supervision can achieve good accuracy.•Lexical and semantic features combined can lead to improved predictive performance.•Under-sampling the major class in training data, can also lead to further improvement.
论文关键词：Semantic indexing,MeSH,Biomedical literature,Weak supervision,00-01,99-00
论文评审过程：Received 27 November 2019, Revised 6 March 2020, Accepted 24 April 2020, Available online 23 May 2020, Version of Record 23 May 2020.
论文官网地址：https://doi.org/10.1016/j.ipm.2020.102282
