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
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.
Fund:National Natural Science Foundation of China(No.61772284,61806101)
Corresponding Authors:
CHEN Kejia, Ph.D., professor. Her research interests include data mining and machine learning with applications in complex network analysis.
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.
QIAO Kai,CHEN Kejia,Chen Jingqiang. Chinese Medical Question Answering Matching Method Based on Knowledge Graph and Keyword Attention Mechanism[J]. , 2021, 34(8): 733-741.
[1] BARRÓN-CEDEÑO 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). Stroudsburg, USA: ACL, 2015: 687-693. [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. Berlin, Germany: Springer, 2012: 1225-1233. [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. Washington, USA: IEEE, 2016: 139-144. [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. Stroudsburg, USA: ACL, 2015: 196-202. [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. Berlin, Germany: Springer, 2014: 51-62. [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. New York, USA: ACM, 2015: 373-382. [7] YIN W P, SCHÜTZE H, XIANG B, et al. ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs. Transactions of the Association for Computational Linguistics, 2016, 4: 259-272. [8] MUELLER J, THYAGARAJAN A. Siamese Recurrent Architectures for Learning Sentence Similarity // Proc of the 30th AAAI Confe-rence on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2016: 2786-2792. [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). Stroudsburg, USA: ACL, 2017: 1657-1668. [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. New York, USA: ACM, 2017: 4144-4150. [11] YANG R Q, ZHANG J H, GAO X, et al. Simple and Effective Text Matching with Richer Alignment Features // Proc of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2019: 4699-4709. [12] MIAO C Y, CAO Z, TAM Y C. Keyword-Attentive Deep Semantic Matching[C/OL]. [2021-03-12]. https://arxiv.org/ftp/arxiv/papers/2003/2003.11516.pdf. [13] DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C/OL]. [2021-03-12]. https://arxiv.org/pdf/1810.04805.pdf. [14] VASWANI A, SHAZEER N, PARMAR N, et al. Attention Is All You Need // Proc of the 31st International Conference on Neural Information Processing Systems. Cambridge, USA: The MIT Press, 2017: 6000-6010. [15] LIU W J, ZHOU P, ZHAO Z, et al. K-BERT: Enabling Language Representation with Knowledge Graph. Proceedings of the AAAI Confe-rence on Artificial Intelligence, 2020, 34(3): 2901-2908. [16] ZHANG Z Y, HAN X, LIU Z Y, et al. ERNIE: Enhanced Language Representation with Informative Entities // Proc of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2019: 1441-1451. [17] FENG M W, XIANG B, GLASS M R, et al. Applying Deep Learning to Answer Selection: A Study and an Open Task // Proc of the IEEE Workshop on Automatic Speech Recognition and Understanding. Washington, USA: IEEE, 2015: 813-820. [18] JOSHI M, CHOI E, LEVY O, et al. pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference[C/OL]. [2021-03-12]. https://arxiv.org/pdf/1810.08854.pdf. [19] LUO R X, XU J J, ZHANG Y, et al. PKUSEG: A Toolkit for Multi-domain Chinese Word Segmentation[C/OL]. [2021-03-12]. https://arxiv.org/pdf/1906.11455.pdf. [20] TENEVA N, CHENG W W. Salience Rank: Efficient Keyphrase Extraction with Topic Modeling // Proc of the 55th Annual Meeting of the Association for Computational Linguistics(Short Papers). Stroudsburg, USA: ACL, 2017: 530-535. [21] HE J Q, FU M M, TU M S. Applying Deep Matching Networks to Chinese Medical Question Answering: A Study and a Dataset. BMC Medical Informatics and Decision Making, 2019, 19(S2): 91-100. [22] LI Y X, TAN C L, DING X Q, et al. Contextual Post-Processing Based on the Confusion Matrix in Offline Handwritten Chinese Script Recognition. Pattern Recognition, 2004, 37(9): 1901-1912. [23] MIHALCEA R, TARAU P. TextRank: Bringing Order into Texts // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2004: 404-411.
2021, Vol. 34 
