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To solve the problem of low precision and inaccurate deep semantic matching of medical question answering system, a question answering model(TIBD-QA) based on TF-IDF, BERT and DSSM was proposed.Child disease care related data in the mother′s network was captured to build child disease care question answering data set ChildQA.The DSSM algorithm was used to solve the problem of low efficiency of artificial feature conversion. The multi-head attention mechanism in BERT enabled the model to focus on different aspects of information and made the context information more accurate.The comparative experimental results showed that the precision rate of the proposed method on ChildQA data set and WebQA public data set reached 83.6% and 84.3% respectively, and achieved good effects in the construction of children′s disease question answering system.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2021317
China Classification Code:R473.72;TP391.1
Citation Information:
[1]ZHANG Xing,WANG Hairong,LI Mingliang ,et al.Construction of Question Answering Model for Children′s Diseases Based on Knowledge Graph[J].Journal of Zhengzhou University(Natural Science Edition),2022,54(02):74-80.DOI:10.13705/j.issn.1671-6841.2021317.
Fund Information:
宁夏自然科学基金项目(2020AAC03218); 宁夏产教融合示范专业项目(2018SFZY14); 北方民族大学教育教学改革重点项目(2019ZDJY01)
2021-11-17
2021-11-17
2021-11-17