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| Chinese Medical Question Answering Matching Method Based on Knowledge Graph and Keyword Attention Mechanism |
| QIAO Kai1, CHEN Kejia1,2, Chen Jingqiang1,2 |
1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023
2. Jiangsu Key Laboratory for Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing 210023 |
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Abstract Due to the lack of high-quality question and answer data in Chinese medical field, a Chinese medical question answering matching method combining knowledge graph and keyword attention mechanism is proposed. Firstly, the medical knowledge graph is introduced into the bidirectional encoder representation from transformers(BERT) model to obtain knowledge-enhanced sentence features, and a keyword attention mechanism is employed to emphasize the interaction between question and answer sentences. The experimental results on two open Chinese medical question-answer datasets, cMedQA and webMedQA , show that the proposed model is obviously better , especially for the small amount of samples. The ablation experiment also verifies that each of the new modules improve the performance of BERT to a certain extent.
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Received: 28 April 2021
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| Fund:National Natural Science Foundation of China(No.61772284,61806101) |
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Corresponding Authors:
CHEN Kejia, Ph.D., professor. Her research interests include data mining and machine learning with applications in complex network analysis.
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| About author:: QIAO Kai, master student. His research interests include knowledge graph, natural language processing and data mining.CHEN Jingqiang, Ph.D., associate profe-ssor. His research interests include summarization, semantic link network and natural language processing. |
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2021, Vol. 34 
