ArticleBioinformatics (Oxford, England)2017
BIOSSES: a semantic sentence similarity estimation system for the biomedical domain.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed.
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- BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
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- MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval.Bioinformatics (Oxford, England) · 2023Article
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- An extensive benchmark study on biomedical text generation and mining with ChatGPT.Bioinformatics (Oxford, England) · 2023Article
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- Fine-tuning large neural language models for biomedical natural language processing.Patterns (New York, N.Y.) · 2023Article
- Standardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- A review on Natural Language Processing Models for COVID-19 research.Healthcare analytics (New York, N.Y.) · 2022Review
- No means 'No': a non-improper modeling approach, with embedded speculative context.Bioinformatics (Oxford, England) · 2022Article
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3 authors.
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Abstract
motivationThe amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English. However, our experiments showed that such approaches do not effectively cover biomedical knowledge and produce poor results for biomedical text.
methodsWe propose several approaches for sentence-level semantic similarity computation in the biomedical domain, including string similarity measures and measures based on the distributed vector representations of sentences learned in an unsupervised manner from a large biomedical corpus. In addition, ontology-based approaches are presented that utilize general and domain-specific ontologies. Finally, a supervised regression based model is developed that effectively combines the different similarity computation metrics. A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods.
resultsThe experiments showed that the supervised semantic sentence similarity computation approach obtained the best performance (0.836 correlation with gold standard human annotations) and improved over the state-of-the-art domain-independent systems up to 42.6% in terms of the Pearson correlation metric. AVAILABILITY AND IMPLEMENTATION: A web-based system for biomedical semantic sentence similarity computation, the source code, and the annotated benchmark data set are available at: http://tabilab.cmpe.boun.edu.tr/BIOSSES/ . CONTACT: gizemsogancioglu@gmail.com or arzucan.ozgur@boun.edu.tr.
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