Drug-Drug Relationship Extraction Based on Capsule Networks
LIU Ningning1, JU Shenggen1, XIONG Xi2,WANG Jingyan1,ZHANG Rui1
1.School of Computer Science, Sichuan University,Chengdu,Sichuan 610065,China; 2.School of Cybersecurity, Chengdu University of Information Technology, Chengdu,Sichuan 610225,China
Abstract:Drug-Drug interaction refers to the inhibition or promotion between drugs. To improve the current Drug-Drug interaction relationship extraction model’s performance in the long sentences, this paper proposes a capsule network extraction model that combines the shortest dependent path. The approach first detects the shortest dependent path between two drugs in the parse of the original sentence, then applies the Bi-LSTM to obtain the embedding of the original sentence and the shortest dependent path. The embedding are them put into the capsule network, in which the dynamic routing mechanism could dynamically determine the amount of information transmitted and preserve the high-level feature information. The experimental results on the DDIExtraction2013 show that the proposed achieved 1.17% relative increase in F1 value compared with the current best approaches.
[1] Law V, Knox C, Djoumbou Y, et al. DrugBank 4.0: Shedding new light on drug metabolism[J]. Nucleic Acids Research, 2013, 42(D1): D1091-D1097. [2] Thorn C F, Klein T E, Altman R B. PharmGKB[J]. Methods in Molecular Biology TM, 2005, 311: 179-191. [3] Segura Bedmar I, Martinez P, Sánchez Cisneros D. The 1st DDIExtraction-2011 challenge task: Extraction of drug-drug Interactions from biomedical texts[C]//Proceedings of the 1st Challenge Task on Drug-Drug Interaction Extraction 2011, 2011(01): 1-9. [4] Segura Bedmar I, Martínez P, Herrero Zazo M. Semeval-2013 task 9: Extraction of drug-drug interactions from biomedical texts (DDIextraction 2013)[C]//Proceedings of the Association for Computational Linguistics, 2013. [5] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]//Proceedings of Advances in Neural Information Processing Systems, 2017: 3856-3866. [6] Zhao W, Ye J, Yang M, et al. Investigating capsule networks with dynamic routing for text classification[J]. arXiv preprint arXiv: 1804.00538,2018. [7] Wang Y, Sun A, Han J, et al. Sentiment analysis by capsules[C]//Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2018: 1165-1174. [8] Wang Y, Sun A, Huang M, et al. Aspect-level sentiment analysis using AS-Capsules[C]//Proceedings of the World Wide Web Conference. ACM, 2019: 2033-2044. [9] Zheng Z, Huang S, Tu Z, et al. Dynamic past and future for neural machine translation[J]. arXiv preprint arXiv: 1904.09646, 2019. [10] Tari L, Anwar S, Liang S, et al. Discovering drug-drug interactions: A text-mining and reasoning approach based on properties of drug metabolism[J]. Bioinformatics, 2010, 26(18): i547-i553. [11] Segura-Bedmar I, Martínez P, de Pablo-Sánchez C. A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents[C]//Proceedings of the BMC Bioinformatics. BioMed Central, 2011, 12(2): S1. [12] He L, Yang Z, Zhao Z, et al. Extracting drug-drug interaction from the biomedical literature using a stacked generalization-based approach[J]. PloS one, 2013, 8(6): e65814. [13] Rastegar-Mojarad M, Boyce R D, Prasad R. UWM-TRIADS: Classifying drug-drug interactions with two-stage SVM and post-processing[C]//Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval 2013), 2013(2): 667-674. [14] Kim S, Liu H, Yeganova L, et al. Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach[J]. Journal of Biomedical Informatics, 2015, 55: 23-30. [15] Chowdhury M F M, Lavelli A. Exploiting the scope of negations and heterogeneous features for relation extraction: A case study for drug-drug interaction extraction[C]//Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2013: 765-771. [16] Yijia Zhang, Hongfei Lin, Zhihao Yang, et al. A single kernel-based approach to extract drug-drug interactions from biomedical literature[J]. PLoS ONE, 2012, 7(11): e48901. [17] Shengyu L, Buzhou T, Qingcai C, et al. Drug-drug interaction extraction via convolutional neural networks[J]. Computational and Mathematical Methods in Medicine, 2016: 1-8. [18] Chanqin Q, Lei H, Xiao S, et al. Multichannel convolutional neural network for biological relation Extraction[J]. BioMed Research International, 2016: 1-10. [19] Kavaluru R, Rios A, Tran T. Extracting drug-drug interactions with word and character-level recurrent neural networks[C]//Proceedings of the 2017 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2017: 5-12. [20] Asada M, Miwa M, Sasaki Y. Extracting drug-drug interactions with attention CNNs[C]//Proceedings of the BioNLP 2017, 2017: 9-18. [21] Zhou D, Miao L, He Y. Position-aware deep multi-task learning for drug-drug interaction extraction[J]. Artificial Intelligence in Medicine, 2018, 87: 1-8. [22] Asada M, Miwa M, Sasaki Y. Enhancing drug-drug interaction extraction from Texts by molecular structure information[J]. arXiv preprint arXiv: 1805.05593, 2018.[23] Xu B, Shi X, Zhao Z, et al. Leveraging biomedical resources in biLSTM for drug-drug interaction extraction[J]. IEEE Access, 2018(6): 33432-33439. [24] Wang W, Yang X, Yang C, et al. Dependency-based long short term memory network for drug-drug interaction extraction[J]. BMC Bioinformatics, 2017, 18(16): 578. [25] Yi Z, Li S, Yu J, et al. Drug-drug interaction extraction via recurrent neural network with multiple attention layers[C]//Proceedings of the International Conference on Advanced Data Mining and Applications. Springer, Cham, 2017: 554-566.