Large-scale investigation of weakly-supervised deep learning for the fine-grained semantic indexing of biomedical literature

• URL: http://arxiv.org/abs/2301.09350v2
• Date: Thu, 5 Oct 2023 14:17:39 GMT
• Title: Large-scale investigation of weakly-supervised deep learning for the fine-grained semantic indexing of biomedical literature
• Authors: Anastasios Nentidis, Thomas Chatzopoulos, Anastasia Krithara, Grigorios Tsoumakas, Georgios Paliouras
• Abstract summary: This study proposes a new method for the automated refinement of subject annotations at the level of MeSH concepts. The new method is evaluated on a large-scale retrospective scenario, based on concepts promoted to descriptors.
• Score: 7.171698704686836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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