ArticleBMC bioinformatics2021
External features enriched model for biomedical question answering.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Question answering systems for health professionals at the point of care-a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Chinese Clinical Named Entity Recognition With Segmentation Synonym Sentence Synthesis Mechanism: Algorithm Development and Validation.JMIR medical informatics · 2024Article
- The Role of Large Language Models in Medical Education: Applications and Implications.JMIR medical education · 2023Article
- ChatGPT and Artificial Intelligence in Medical Writing: Concerns and Ethical Considerations.Cureus · 2023Article
- Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study.International journal of environmental research and public health · 2023Article
- BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task.Bioinformatics (Oxford, England) · 2022Article
- YTLR: Extracting yeast transcription factor-gene associations from the literature using automated literature readers.Computational and structural biotechnology journal · 2022Article
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Authors and funding
5 authors.
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Abstract
backgroundBiomedical question answering (QA) is a sub-task of natural language processing in a specific domain, which aims to answer a question in the biomedical field based on one or more related passages and can provide people with accurate healthcare-related information. Recently, a lot of approaches based on the neural network and large scale pre-trained language model have largely improved its performance. However, considering the lexical characteristics of biomedical corpus and its small scale dataset, there is still much improvement room for biomedical QA tasks.
resultsInspired by the importance of syntactic and lexical features in the biomedical corpus, we proposed a new framework to extract external features, such as part-of-speech and named-entity recognition, and fused them with the original text representation encoded by pre-trained language model, to enhance the biomedical question answering performance. Our model achieves an overall improvement of all three metrics on BioASQ 6b, 7b, and 8b factoid question answering tasks.
conclusionsThe experiments on BioASQ question answering dataset demonstrated the effectiveness of our external feature-enriched framework. It is proven by the experiments conducted that external lexical and syntactic features can improve Pre-trained Language Model's performance in biomedical domain question answering task.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.