Abstract
With the rapid development of the Internet, community question answering (CQA) platforms have attracted increasing attention over recent years, particularly in the biomedical field. On biomedical CQA platforms, patients share information about diseases, drugs and symptoms by communicating with each other. Therefore, the biomedical CQA platforms become particularly valuable resources for information and knowledge acquisition of patients. To accurately acquire relevant information, question answering techniques have been introduced in biomedical CQA. However, existing approaches cannot achieve the ideal performance due to the domain-specific characteristics. For example, biomedical CQA involves more complex interactive information between askers and answerers, while CQA techniques designed for the general field can only deal with single interactions between questions and candidate answers within a similar topic. To address the problem, we propose a novel neural network model for biomedical CQA. Our model adopts the bidirectional capsule network to focus on different aspects of biomedical questions and candidate answers, and merges high-level vector representations of questions and answers to capture abundant semantic information. Furthermore, to capture the meaning of Chinese characters, we incorporate the radical of Chinese characters embedding as auxiliary information to improve the performance of Chinese biomedical CQA. We conduct extensive experiments, and demonstrate that our model achieves significant improvement on the performance of answer selection in the Chinese biomedical CQA task.
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References
Bhandwaldar, A., Zadrozny, W.: UNCC QA: biomedical question answering system. In: Proceedings of the 6th BioASQ Workshop A Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering, pp. 66–71 (2018)
Jin, Z.X., Zhang, B.W., Fang, F., et al.: Health assistant: answering your questions anytime from biomedical literature. Bioinformatics (2019)
Chen, L., Jose, J.M., Yu, H., et al.: A semantic graph based topic model for question retrieval in community question answering. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 287–296. ACM (2016)
Lai, T.M., Bui, T., Li, S.: A review on deep learning techniques applied to answer selection. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2132–2144 (2018)
Sequiera, R., Baruah, G., Tu, Z., et al.: Exploring the effectiveness of convolutional neural networks for answer selection in end-to-end question answering. arXiv preprint arXiv:1707.07804 (2017)
Fang, H., Wu, F., Zhao, Z., et al.: Community-based question answering via heterogeneous social network learning. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Zhao, Z., Lu, H., Zheng, V.W., et al.: Community-based question answering via asymmetric multi-faceted ranking network learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Maleewong, K.: Predicting quality-assured consensual answers in community-based question answering systems. In: Meesad, P., Boonkrong, S., Unger, H. (eds.) Recent Advances in Information and Communication Technology. Advances in Intelligent Systems and Computing, pp. 117–127. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40415-8_12
Tran, Q.H., Lai, T., Haffari, G., et al.: The context-dependent additive recurrent neural net. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1274–1283 (2018)
Tan, M., Dos Santos, C., Xiang, B., et al.: Improved representation learning for question answer matching. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 464–473 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Liwei, Y., Lei, S., Peng, S.: Attribute reduction for Chinese question classification. In: 2016 Chinese Control and Decision Conference (CCDC), pp. 5488–5492. IEEE (2016
Yu, L., Hermann, K.M., Blunsom, P., et al.: Deep learning for answer sentence selection. arXiv preprint arXiv:1412.1632 (2014)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3856–3866 (2017)
Zhao, W., Ye, J., Yang, M., et al.: Investigating capsule networks with dynamic routing for text classification. arXiv preprint arXiv:1804.00538 (2018)
Zhang, X., Li, P., Jia, W., et al.: Multi-labeled relation extraction with attentive capsule network. arXiv preprint arXiv:1811.04354 (2018)
Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Li, Y., Li, W., Sun, F., et al.: Component-enhanced chinese character embeddings. arXiv preprint arXiv:1508.06669 (2015)
Dong, C., Zhang, J., Zong, C., Hattori, M., Di, H.: Character-based LSTM-CRF with radical-level features for Chinese named entity recognition. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC-2016. LNCS (LNAI), vol. 10102, pp. 239–250. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_20
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks 5(2), 157–166 (1994)
Santos, C., Tan, M., Xiang, B., et al.: Attentive pooling networks. arXiv preprint arXiv:1602.03609 (2016)
Hinton, G.E., Krizhevsky, A., Wang, S.D.: Transforming auto-encoders. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 44–51. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21735-7_6
Conneau, A., Kiela, D., Schwenk, H., et al.: Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02364 (2017)
Ren, Y., Zhang, T., Liu, X., et al.: End-to-end answer selection via attention-based Bi-LSTM network. In: 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN), pp. 264–265. IEEE (2018)
Acknowledgments
This work is partially supported by grant from the Natural Science Foundation of China (No. 61572102, 61702080, 61772103), the Postdoctoral Science Foundation of China (No. 2018M641691), the Foundation of State Key Laboratory of Cognitive Intelligence, iFLYTEK, P.R. China (COGOS-20190001).
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Zhang, T. et al. (2019). Bi-directional Capsule Network Model for Chinese Biomedical Community Question Answering. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_9
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DOI: https://doi.org/10.1007/978-3-030-32233-5_9
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