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Updated: Jul 16, 2025
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
Large-scale investigation of weakly-supervised deep learning for the fine-grained semantic indexing of biomedical
Anastasios Nentidis1, Thomas Chatzopoulos2, Anastasia Krithara3
1Institute of Informatics and Telecommunications, NCSR Demokritos, Athens, Greece; School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Objective:
Semantic indexing of biomedical literature is usually done at the level of MeSH descriptors with several related but distinct biomedical concepts often grouped together and treated as a single topic. This study proposes a new method for the automated refinement of subject annotations at the level of MeSH concepts.
Methods:
Lacking labelled data, we rely on weak supervision based on concept occurrence in the abstract of an article, which is also enhanced by dictionary-based heuristics. In addition, we investigate deep learning approaches, making design choices to tackle the particular challenges of this task. The new method is evaluated on a large-scale retrospective scenario, based on concepts that have been promoted to descriptors.
Results:
In our experiments concept occurrence was the strongest heuristic achieving a macro-F1 score of about 0.63 across several labels. The proposed method improved it further by more than 4pp.
Conclusion:
The results suggest that concept occurrence is a strong heuristic for refining the coarse-grained labels at the level of MeSH concepts and the proposed method improves it further.
