Chinese Medical Question Answering Matching Method Based on Knowledge Graph and Keyword Attention Mechanism

被引：0
作者：
Qiao K. [1 ]
Chen K. [1 ,2 ]
Chen J. [1 ,2 ]
机构：
[1] School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing
[2] Jiangsu Key Laboratory for Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing
来源：
Chen, Kejia (chenkj@njupt.edu.cn); Chen, Kejia (chenkj@njupt.edu.cn) | 1600年 / Science Press卷 / 34期
基金：
中国国家自然科学基金;
关键词：
Attention Mechanism; Knowledge Graph; Natural Language Processing; Question-and-Answer Pair Matching;
D O I：
10.16451/j.cnki.issn1003-6059.202108006
中图分类号：
学科分类号：
摘要：
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. © 2021, Science Press. All right reserved.
引用
收藏
页码：733 / 741
页数：8
相关论文
共 23 条
[1]  
BARRON-CEDENO A, FILICE S, DA SAN MARTINO G, Et al., Thread-Level Information for Comment Classification in Community Question Answering, Proc of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing(Short Papers), pp. 687-693, (2015)
[2]  
JAIN S, DODIYA T., Rule Based Architecture for Medical Question Answering System, Proc of the 2nd International Conference on Soft Computing for Problem Solving, pp. 1225-1233, (2012)
[3]  
WANG J, MAN C T, ZHAO Y F, Et al., An Answer Recommendation Algorithm for Medical Community Question Answering Systems, Proc of the IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 139-144, (2016)
[4]  
HOU Y S, TAN C, WANG X L, Et al., HITSZ-ICRC: Exploiting Classification Approach for Answer Selection in Community Question Answering, Proc of the 9th International Workshop on Semantic Evaluation, pp. 196-202, (2015)
[5]  
YIN Y S, ZHANG Y, LIU X, Et al., HealthQA: A Chinese QA Summary System for Smart Health, Proc of the International Conference on Smart Health, pp. 51-62, (2014)
[6]  
SEVERYN A, MOSCHITTI A., Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks, Proc of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 373-382, (2015)
[7]  
YIN W P, SCHUTZE H, XIANG B, Et al., ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs, Transactions of the Association for Computational Linguistics, 4, pp. 259-272, (2016)
[8]  
MUELLER J, THYAGARAJAN A., Siamese Recurrent Architectures for Learning Sentence Similarity, Proc of the 30th AAAI Confe-rence on Artificial Intelligence, pp. 2786-2792, (2016)
[9]  
CHEN Q, ZHU X D, LING Z H, Et al., Enhanced LSTM for Natural Language Inference, Proc of the 55th Annual Meeting of the Association for Computational Linguistics(Long Papers), pp. 1657-1668, (2017)
[10]  
WANG Z G, HAMZA W, FLORIAN R., Bilateral Multi-perspective Matching for Natural Language Sentences, Proc of the 26th International Joint Conference on Artificial Intelligence, pp. 4144-4150, (2017)
123