Beyond MeSH: Fine-grained semantic indexing of biomedical literature based on weak supervision. Issue 5 (September 2020)

• Record Type: Journal Article
• Authors: Nentidis, Anastasios; Krithara, Anastasia; Tsoumakas, Grigorios; Paliouras, Georgios
• Is Part Of: Information processing & management. Volume 57:Issue 5(2020:Sep.)
• Publication Date: 2020-09
• Subjects: Semantic indexing -- MeSH -- Biomedical literature -- Weak supervision; 00-01 -- 99-00; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102282
• Languages: English
• ISSNs: 0306-4573

Abstract

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. Abstract: In this work, we propose a method for the automated refinement of subject annotations in biomedical literature at the level of concepts. Semantic indexing and search of biomedical articles in MEDLINE/PubMed are based on semantic subject annotations with MeSH descriptors that may correspond to several related but distinct biomedical concepts. Such semantic annotations do not adhere to the level of detail available in the domain knowledge and may not be sufficient to fulfil the information needs of experts in the domain. To this end, we propose a new method that uses weak supervision to train a concept annotator on the literature available for a particular disease. We test this method on the MeSH descriptors for two diseases: Alzheimer's Disease and Duchenne Muscular Dystrophy. The results indicate that concept-occurrence is a strong heuristic for automated subject annotation refinement and its use as weak supervision can lead to improved concept-level annotations. The fine-grained semantic annotations can enable more precise literature retrieval, sustain the semantic integration of subject annotations with other domain resources and ease the maintenance of consistent subject annotations, as new more detailed entries are added in the MeSH thesaurus over time.