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研究生:盧韋良
研究生(外文):Wei-Liang Lu
論文名稱:利用上下文感知最大化邊界神經網路提取疾病與疾病的關聯
論文名稱(外文):Extracting disease-disease associations with context-aware max-margin neural network
指導教授:洪炯宗洪炯宗引用關係
指導教授(外文):Jorng-Tzong Horng
學位類別:碩士
校院名稱:國立中央大學
系所名稱:軟體工程研究所
學門:電算機學門
學類:軟體發展學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:49
中文關鍵詞:自然語言處理文字探勘機器學習深度學習
外文關鍵詞:natural language processingtext miningmachine learningdeep learning
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由於缺乏人工標註高品質的疾病之間關聯語料庫,在本篇論文中我們建構了一個疾病之間關聯語料庫,並用於建構與評估我們的系統。最後我們建一個末端對末端(End-to-end)的最大化邊界上下文感知神經網絡。在我們的實驗結果顯示相較於單純的卷積類神經網路而言,支持向量機達到 77.82% F1度量,高於CNN模型 2.47% F1度量。接著我們將卷積類神經網路的結果作為特徵值加入支持向量機分類元件中,檢查是否可以提升分類效果,而最好的實驗結果為 77.32% F1度量,比只使用該特徵值的支持向量機低 0.5% F1 度量,主要原因是在訓練支持向量機的同時無法同步更新類神經網路,導致分類效果沒有提升。因此我們建構一末端對末端最大化邊界上下文感知神經網絡來分類疾病關聯,達到最高的 84.34% F1度量,精確度80.65%和召回率88.39%。
In our study, we constructed a disease-association corpus then use it to build and evaluate the disease-association extraction system. Finally, we propose a max-margin context-aware neural network. The results show that the support vector machine(SVM) achieves the highest F1-measure of 77.82%. The SVM-based approach is higher than the convolutional neural networks(CNN) by F1-measure of 2.47%. Then we merge the softmax layer of CNN as feature to the SVM then check whether the performance was improved or not. However, the best result is an F1-measure of 77.32%, which is 0.5% lower than the original SVM which using only its feature. The possible reason may be the NN can’t be updated synchronously while training the SVM. Therefore, we constructed a max-margin context-aware neural network to classify disease associations and achieve the highest F1-measure of 84.34%.
摘要 i
Abstract ii
致謝 iii
Table of contents iv
List of figures v
List of tables vi
Chapter 1. Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Goal 2
Chapter 2. Related works 3
Chapter 3. Methods 5
3.1 System architecture 5
3.2 Data collection 6
3.3 Data filtering 6
3.4 Feature and embedding 8
3.4.1 Pair embedding 9
3.4.1 Sentence embedding 13
3.5 Relation extraction 16
Chapter 4. Experiment setup 18
Chapter 5. Results 19
5.1 Comparison of different baseline models 19
5.2 Comparison of combine models 20
5.3 Comparison of max-margin models 21
Chapter 6. Discussions 22
Chapter 7. Conclusion 24
References 25
Appendix 28
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3. Chillarón, J., et al., Insulin resistance and hypertension in patients with type 1 diabetes. Journal of diabetes and its complications, 2011. 25(4): p. 232.
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9. Pyysalo, S., et al., BioInfer: a corpus for information extraction in the biomedical domain. BMC bioinformatics, 2007. 8(1): p. 50.
10. Wishart, D.S., et al., DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic acids research, 2017. 46(D1): p. D1074-D1082.
11. Pyysalo, S., T. Ohta, and S. Ananiadou. Overview of the cancer genetics (CG) task of BioNLP Shared Task 2013. in Proceedings of the BioNLP Shared Task 2013 Workshop. 2013.
12. Li, J., et al., BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database, 2016.
13. Peng, Y., C.-H. Wei, and Z. Lu, Improving chemical disease relation extraction with rich features and weakly labeled data. Journal of cheminformatics, 2016. 8(1): p. 53.
14. Pons, E., et al., Extraction of chemical-induced diseases using prior knowledge and textual information. Database, 2016: p. baw046.
15. Xu, J., et al., CD-REST: a system for extracting chemical-induced disease relation in literature. Database, 2016.
16. Asada, M., M. Miwa, and Y. Sasaki, Extracting Drug-Drug Interactions with Attention CNNs. BioNLP, 2017: p. 9-18.
17. Peng, Y. and Z. Lu, Deep learning for extracting protein-protein interactions from biomedical literature. BioNLP, 2017: p. 29-38.
18. Zhao, Z., et al., Drug drug interaction extraction from biomedical literature using syntax convolutional neural network. Bioinformatics, 2016. 32(22): p. 3444-3453.
19. Peng, Y., et al., Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models. arXiv preprint arXiv:1802.01255, 2018.
20. Feng, Z., Z. Sun, and L. Jin. Learning deep neural network using max-margin minimum classification error. in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. 2016. IEEE.
21. Tang, Y., Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239, 2013.
22. Lee, J., et al. On the efficacy of per-relation basis performance evaluation for PPI extraction and a high-precision rule-based approach. in BMC medical informatics and decision making. 2013. BioMed Central.
23. Nguyen, N.T., et al., Wide-coverage relation extraction from MEDLINE using deep syntax. BMC bioinformatics, 2015. 16(1): p. 107.
24. Lipscomb, C.E., Medical subject headings (MeSH). Bulletin of the Medical Library Association, 2000. 88(3): p. 265.
25. Davis, A.P., et al., The comparative toxicogenomics database: update 2017. Nucleic acids research, 2016. 45(D1): p. D972-D978.
26. Leaman, R., R. Islamaj Doğan, and Z. Lu, DNorm: disease name normalization with pairwise learning to rank. Bioinformatics, 2013. 29(22): p. 2909-2917.
27. Mikolov, T., et al. Distributed representations of words and phrases and their compositionality. in Advances in neural information processing systems. 2013.
28. Mikolov, T., et al., Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
29. Mikolov, T., W.-t. Yih, and G. Zweig. Linguistic regularities in continuous space word representations. in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2013.
30. Mikolov, T., et al. Recurrent neural network based language model. in Eleventh Annual Conference of the International Speech Communication Association. 2010.
31. SPFGH, M. and T.S.S. Ananiadou, Distributional semantics resources for biomedical text processing. 2013.
32. Peng, Y. and Z. Lu, Deep learning for extracting protein-protein interactions from biomedical literature. arXiv preprint arXiv:1706.01556, 2017.
33. Chang, C.-C. and C.-J. Lin, LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2011. 2(3): p. 27.
34. Xu, M., et al., Deep Learning for Person Reidentification Using Support Vector Machines. Advances in Multimedia, 2017.
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