簡易檢索 / 詳目顯示

研究生: 班法
Bamfa Ceesay
論文名稱: Exploring Biomedical Text Processing and Event Extraction
Exploring Biomedical Text Processing and Event Extraction
指導教授: 侯文娟
Hou, Wen-Juan
學位類別: 博士
Doctor
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 英文
論文頁數: 144
英文關鍵詞: DDI extraction, Biomedical Text, Adaptation of RNN, Domain transformation, Unstable gradient, BioNLP, Neural embedding
DOI URL: http://doi.org/10.6345/NTNU202000546
論文種類: 學術論文
相關次數: 點閱:122下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • Biomedical text mining (including biomedical natural language processing or BioNLP) refers to the methods and study of how text mining may be applied to texts and literature of the biomedical and molecular biology domains. As a field of research, biomedical text mining incorporates ideas from natural language processing, bioinformatics, medical informatics and computational linguistics. With the enormous volume of biological literature, an increasing growth phenomenon due to the high rate of new publications is one of the most common motivations for the biomedical text mining. Using these massive literatures available, biological information could be extracted using various research algorithms and text mining techniques. Recent studies have seen significant adaption of neural methods in many machine learning methods. Significant results and performance improvements have been achieved with neural networks. In this PhD dissertation, we intend to explore a general perspective of BioIE in NLP and the application of neural methodologies in BioNLP. We shall survey and set up experimental models to investigate NLP methodologies and approaches in BioIE.

    Abstract i Dedication ii Declaration iii Table of Content iv List of Tables vi List of Figures viii Chapter 1. Introduction 1 1.1 General Text Pre-Processing Techniques in BioNLP 4 1.2 BioNLP Text Mining Tasks 8 1.3 Evaluation in Natural Language Processing 13 1.4 Our Research Objectives in Drug-Drug Interaction extraction and Event and Relationship Extraction 16 Chapter 2. Literature Review 20 2.1 Research Trend in BioNLP 22 2.2 Application of Neural Network and Deep Learning Methodologies in BioNLP 27 2.3 Review Notes 36 Chapter 3. Extraction of Drug-drug Interaction Using Neural Embedding 38 3.1 Introduction 38 3.2 Experimental Data 43 3.3 System Architecture 45 3.4 Methods 48 3.5 Experimental Results 61 3.6 Discussion 65 Chapter 4. Exploring the Adaptation of Recurrent Neural Network Approaches for Extracting Drug–Drug Interactions from Biomedical Text 67 4.1 Introduction 67 4.2 Method 71 4.3 Classification and Prediction 77 4.4 Experimental Results 78 4.5 Discussion 81 Chapter 5. Domain Transformation on Biological Event Extraction by Learning Methods 82 5.1 Introduction 82 5.2 Event Extraction 87 5.3 Domain Transformation 88 5.4 Transfer Learning 89 5.5 Research Method 92 5.6 Experimental Results 112 5.7 Discussion 115 Chapter 6. Conclusion and Discussion 118 References 122

    [1] Cohen, Kevin Bretonnel and Demner-Fushman, Dina, Biomedical natural language processing, John Benjamins, 2014.
    [2] Sixta, Sherry L and Hatch, Quinton M and Matijevic, Nena and Wade, Charles E and Holcomb, John B and Cotton, Bryan A, "Mechanistic determinates of the acute coagulopathy of trauma (ACoT) in patients requiring emergency surgery," International journal of burns and trauma, vol. 2, p. 158, 2012.
    [3] Tsujii, Jun'ichi and Cohen, K. Bretonnel and Hunter, Lawrence and McCrohon, Luke and Ananiadou, Sophia and Baumgartner, William A., Jr and Kano, Yoshinobu, "U-Compare: share and compare text mining tools with UIMA," Bioinformatics, vol. 25, pp. 1997-1998, 05 2009.
    [4] Bukhari, Ahmad C and Klein, Artjom and Baker Christopher J. O., "Towards Interoperable BioNLP Semantic Web Services Using the SADI Framework," in Data Integration in the Life Sciences, Springer Berlin Heidelberg, 2013, pp. 69-80.
    [5] Zhu, Muhua and Zhang, Yue and Chen, Wenliang and Zhang, Min and Zhu, Jingbo, "Fast and accurate shift-reduce constituent parsing," in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, 2013, pp. 434--443.
    [6] Nivre, j, "Dependency grammar and dependency parsing," MSI report, vol. S133, pp. 1-39, 1959.
    [7] Bodenreider, O, "Lexical, terminological and ontological resources for biological text mining," Text mining for biology and biomedicine, pp. 43-66, 2006.
    [8] Liu, Hongfang and Johnson, Stephen B and Friedman, Carol, "Automatic resolution of ambiguous terms based on machine learning and conceptual relations in the UMLS," Journal of the American Medical Informatics Association, vol. 9, pp. 621-636, 2002.
    [9] Spasic, Irena and Ananiadou, Sophia and McNaught, John and Kumar, Anand, "Text mining and ontologies in biomedicine: making sense of raw text," Briefings in bioinformatics, vol. 6, pp. 239-251, 2005.
    [10] Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel, "Introduction to the bio-entity recognition task at JNLPBA," in Proceedings of the international joint workshop on natural language processing in biomedicine and its applications, Citeseer, 2004, pp. 70--75.
    [11] Skusa, Andre and Ruegg, Alexander and Kohler, Jacob, "Extraction of biological interaction networks from scientific literature," Briefings in bioinformatics, vol. 6, pp. 263--276, 2005.
    [12] Fukuda, Ken-ichiro and Tsunoda, Tatsuhiko and Tamura, Ayuchi and Takagi, Toshihisa and others, "Toward information extraction: identifying protein names from biological papers," in Pac symp biocomput, vol. 707, 1998, pp. 707--718.
    [13] Leser, Ulf and Hakenberg, Jorg, "What makes a gene name? Named entity recognition in the biomedical literature," Briefings in bioinformatics, vol. 6, pp. 357--369, 2005.
    [14] Craven, Mark and Kumlien, Johan and others, "Constructing biological knowledge bases by extracting information from text sources.," in ISMB, vol. 1999, 1999, pp. 77-86.
    [15] Blaschke, Christian and Andrade, Miguel A and Ouzounis, Christos A and Valencia, Alfonso, "Automatic extraction of biological information from scientific text: protein-protein interactions.," in Ismb, vol. 7, 1999, pp. 60-67.
    [16] Kim, Jin-Dong and Ohta, Tomoko and Pyysalo, Sampo and Kano, Yoshinobu and Tsujii, Jun'ichi, "Overview of BioNLP'09 shared task on event extraction," in Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task, Association for Computational Linguistics, 2009, pp. 1--9.
    [17] Kim, Jin-Dong and Wang, Yue and Takagi, Toshihisa and Yonezawa, Akinori, "Overview of genia event task in bionlp shared task 2011," in Proceedings of the BioNLP Shared Task 2011 Workshop, Association for Computational Linguistics, 2011, pp. 7--15.
    [18] Nedellec, Claire and Bossy, Robert and Kim, Jin-Dong and Kim, Jung-Jae and Ohta, Tomoko and Pyysalo, Sampo and Zweigenbaum, Pierre, "Overview of BioNLP shared task 2013," in Proceedings of the BioNLP Shared Task 2013 Workshop, 2013, pp. 1--7.
    [19] Chaix, Estelle and Dubreucq, Bertrand and Fatihi, Abdelhak and Valsamou, Dialekti and Bossy, Robert and Ba, Mouhamadou and Deleger, Louise and Zweigenbaum, Pierre and Bessieres, Philippe and Lepiniec, Loic and others, "Overview of the Regulatory Network of Plant Seed Development (SeeDev) Task at the BioNLP Shared Task 2016.," in Proceedings of the 4th bionlp shared task workshop, 2016, pp. 1--11.
    [20] Demner-Fushman, Dina and Abhyankar, Swapna and Jimeno-Yepes, Antonio and Loane, Russell F and Rance, Bastien and Lang, Franccois-Michel and Ide, Nicholas C and Apostolova, Emilia and Aronson, Alan R, "A Knowledge-Based Approach to Medical Records Retrieval.," in TREC, 2011.
    [21] Friedman, Carol and Alderson, Philip O and Austin, John HM and Cimino, James J and Johnson, Stephen B, "A general natural-language text processor for clinical radiology," Journal of the American Medical Informatics Association, vol. 1, pp. 161--174, 1994.
    [22] N. D. Glenn, Cohort analysis, vol. 5, Sage, 2005.
    [23] Boon, Kathy and Bailey, Nathaniel W and Yang, Jun and Steel, Mark P and Groshong, Steve and Kervitsky, Dolly and Brown, Kevin K and Schwarz, Marvin I and Schwartz, David A, "Molecular phenotypes distinguish patients with relatively stable from progressive idiopathic pulmonary fibrosis (IPF)," PloS one, vol. 4, p. 5134, 2009.
    [24] Wu, Stephen T and Juhn, Young J and Sohn, Sunghwan and Liu, Hongfang, "Patient-level temporal aggregation for text-based asthma status ascertainment," Journal of the American Medical Informatics Association, vol. 5, pp. 876--884, 2014.
    [25] N. N. C. C. (n2c2), "2018 Track 1: Cohort Selection for Clinical Trials," National NLP Clinical Challenges (n2c2), 2018. [Online]. Available: https://portal.dbmi.hms.harvard.edu/projects/n2c2-t1/.
    [26] Bordea, Georgeta and Lefever, Els and Buitelaar, Paul, "Semeval-2016 task 13: Taxonomy extraction evaluation (texeval-2)," in Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016, pp. 1081-1091.
    [27] Mitamura, Teruko and Liu, Zhengzhong and Hovy, Eduard H, "Overview of TAC KBP 2015 Event Nugget Track.," in TAC, 2015.
    [28] W. Hersh, "Information Retrieval from Electronic Health Records for Patient Cohort Discovery," 2019.
    [29] Kim, Jin-Dong and Pyysalo, Sampo, "Bionlp shared task," Encyclopedia of Systems Biology, pp. 138--141, 2013.
    [30] Bengio, Yoshua and Courville, Aaron and Vincent, Pascal, "Representation learning: A review and new perspectives," IEEE transactions on pattern analysis and machine intelligence, vol. 35, pp. 1798--1828, 2013.
    [31] Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron, Deep learning, MIT press, 2016.
    [32] Goldberg Y, "A primer on neural network models for natural language processing," Journal of Artificial Intelligence Research, vol. 57, pp. 345--420, 2016.
    [33] Bossy, Robert and Golik, Wiktoria and Ratkovic, Zorana and Valsamou, Dialekti and Bessi`eres, Philippe and N'edellec, Claire, "Overview of the gene regulation network and the bacteria biotope tasks in bionlp'13 shared task," BMC bioinformatics, vol. 16, p. 51, 2015.
    [34] Prakash, Aaditya and Zhao, Siyuan and Hasan, Sadid A and Datla, Vivek and Lee, Kathy and Qadir, Ashequl and Liu, Joey and Farri, Oladimeji, "Condensed memory networks for clinical diagnostic inferencing," in Thirty-First AAAI Conference on Artificial Intelligence, 2017.
    [35] Lipton, Zachary C and Kale, David C and Elkan, Charles and Wetzel, Randall, "Learning to diagnose with LSTM recurrent neural networks," arXiv preprint arXiv:1511.03677, 2015.
    [36] Choi, Edward and Bahadori, Mohammad Taha and Schuetz, Andy and Stewart, Walter F and Sun, Jimeng, "Doctor ai: Predicting clinical events via recurrent neural networks," in Machine Learning for Healthcare Conference, 2016, pp. 301--318.
    [37] Choi, Edward and Bahadori, Mohammad Taha and Sun, Jimeng and Kulas, Joshua and Schuetz, Andy and Stewart, Walter, "Retain: An interpretable predictive model for healthcare using reverse time attention mechanism," in Advances in Neural Information Processing Systems, 2016, pp. 3504--3512.
    [38] Roberts, Kirk and Simpson, Matthew and Demner-Fushman, Dina and Voorhees, Ellen and Hersh, William, "State-of-the-art in biomedical literature retrieval for clinical cases: a survey of the TREC 2014 CDS track," Information Retrieval Journal, vol. 19, pp. 113--148, 2016.
    [39] Hasan, Sadid A and Zhao, Siyuan and Datla, Vivek V and Liu, Joey and Lee, Kathy and Qadir, Ashequl and Prakash, Aaditya and Farri, Oladimeji, "Clinical Question Answering using Key-Value Memory Networks and Knowledge Graph," in TREC, 2016.
    [40] Li, Zichao and Jiang, Xin and Shang, Lifeng and Li, Hang, "Paraphrase generation with deep reinforcement learning," arXiv preprint arXiv:1711.00279, 2017.
    [41] McKeown, K.R, "Paraphrasing questions using given and new information," Computational Linguistics, vol. 9, pp. 1-10, 1983.
    [42] Bolshakov, Igor A and Gelbukh, Alexander, "Synonymous paraphrasing using wordnet and internet," in International Conference on Application of Natural Language to Information Systems, Springer, 2004, pp. 312--323.
    [43] Miller, George A and Beckwith, Richard and Fellbaum, Christiane and Gross, Derek and Miller, Katherine J, "Introduction to WordNet: An on-line lexical database," International journal of lexicography, vol. 3, pp. 235--244, 1990.
    [44] Narayan, Shashi and Reddy, Siva and Cohen, Shay B, "Paraphrase generation from Latent-Variable PCFGs for semantic parsing," arXiv preprint arXiv:1601.06068, 2016.
    [45] Behzadi, F, "Natural language processing and machine learning: A review," International Journal of Computer Science and Information Security, vol. 13, p. 101, 2015.
    [46] Srinivasamurthy, Naveen and Ortega, Antonio and Narayanan, Shrikanth, "Efficient scalable encoding for distributed speech recognition," Speech Communication, vol. 48, pp. 888--902, 2006.
    [47] Patents, G. , "On-the-fly one-hot encoding of leading zero count". US Patent US Patent 5,974,432, 26 Oct 1999.
    [48] Rong, X. , "word2vec parameter learning explained," arXiv preprint arXiv:1411.2738, 2014.
    [49] Mikolov, Tomas and Chen, Kai and Corrado, Greg and Dean, Jeffrey, "Efficient estimation of word representations in vector space," arXiv preprint arXiv:1301.3781, 2013.
    [50] Guzzetta. F., "Behavioral assessment of language brain processing in the first year of life," european journal of paediatric neurology, vol. 18, pp. 551 -- 557, 2014.
    [51] Kiros, Ryan and Zhu, Yukun and Salakhutdinov, Ruslan R and Zemel, Richard and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja, "Skip-thought vectors," in Advances in neural information processing systems, 2015, pp. 3294--3302.
    [52] Minsky, Marvin and Papert, Seymour A, "Perceptrons: An introduction to computational geometry," MIT press, 2017.
    [53] Britz, D., "Understanding convolutional neural networks for NLP," URL: http://www. wildml. com/2015/11/understanding-convolutional-neuralnetworks-for-nlp/(visited on 11/07/2015), 2015.
    [54] Zhang, Yu and Chen, Guoguo and Yu, Dong and Yaco, Kaisheng and Khudanpur, Sanjeev and Glass, James, "Highway long short-term memory rnns for distant speech recognition," in 016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2016, pp. 5755--5759.
    [55] Feng, Jun and Huang, Minlie and Zhao, Li and Yang, Yang and Zhu, Xiaoyan, "Reinforcement learning for relation classification from noisy data," in Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
    [56] Nallapati, Ramesh and Zhou, Bowen and Gulcehre, Caglar and Xiang, Bing and others, "Abstractive text summarization using sequence-to-sequence rnns and beyond," arXiv preprint arXiv:1602.06023, 2016.
    [57] Karpathy, Andrej and Johnson, Justin and Fei-Fei, Li, "Visualizing and understanding recurrent networks," arXiv preprint arXiv:1506.02078, 2015.
    [58] Feng, Jun and Huang, Minlie and Zhao, Li and Yang, Yang and Zhu, Xiaoyan, "Reinforcement learning for relation classification from noisy data," in Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
    [59] Chen and Jim X, "The evolution of computing: AlphaGo," Computing in Science and Engineering, vol. 18, 2016.
    [60] Wolf and Mark JP, "Genre and the video game," The medium of the video game, pp. 113--134, 2001.
    [61] Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua, "Generative adversarial nets," in Advances in neural information processing systems, 2014, pp. 2672--2680.
    [62] Goodfellow, I, "NIPS 2016 tutorial: Generative adversarial networks," arXiv preprint arXiv:1701.00160, 2016.
    [63] Sanchez-Lengeling, Benjamin and Outeiral, Carlos and Guimaraes, Gabriel L and Aspuru-Guzik, Alan, "Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models," arXiv preprint arXiv:1705.10843, 2017.
    [64] Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097--1105.
    [65] Edwards, I Ralph and Aronson, Jeffrey K, "Adverse drug reactions: definitions, diagnosis, and management," The lancet, vol. 9237, pp. 1255--1259, 2000.
    [66] de Leon, Jose, "Highlights of drug package inserts and the website DailyMed: the need for further improvement in package inserts to help busy prescribers," Journal of clinical psychopharmacology, vol. 31, 2011.
    [67] Wishart, David S and Knox, Craig and Guo, An Chi and Cheng, Dean and Shrivastava, Savita and Tzur, Dan and Gautam, Bijaya and Hassanali, Murtaza, "DrugBank: a knowledgebase for drugs, drug actions and drug targets," Nucleic acids research, vol. 36, pp. D901--D906, 2007.
    [68] Kongsholm, Gertrud Gansmo and Nielsen, Anna Katrine Toft and Damkier, Per, "Drug interaction databases in medical literature: transparency of ownership, funding, classification algorithms, level of documentation, and staff qualifications. A systematic review," European journal of clinical pharmacology, vol. 71, pp. 1397--1402, 2015.
    [69] Prince, B and Makrides, L and Richman, J, "Research methodology and applied statistics. Part 2: the literature search," Physiotherapy Canada, pp. 201--206, 1980.
    [70] Segura Bedmar, Isabel and Martinez, Paloma and Herrero Zazo, Maria, "Semeval-2013 task 9: Extraction of drug-drug interactions from biomedical texts (ddiextraction 2013)," Association for Computational Linguistics, 2013.
    [71] Abdelaziz, Ibrahim and Fokoue, Achille and Hassanzadeh, Oktie and Zhang, Ping and Sadoghi, Mohammad, "Large-scale structural and textual similarity-based mining of knowledge graph to predict drug--drug interactions," Journal of Web Semantics, vol. 44, pp. 104--117, 2017.
    [72] Cao, D-S and Xiao, N and Li, Y-J and Zeng, W-B and Liang, Y-Z and Lu, A-P and Xu, Q-S and Chen, AF, "Integrating multiple evidence sources to predict adverse drug reactions based on a systems pharmacology model," CPT: pharmacometrics \& systems pharmacology, vol. 4, pp. 498--506, 2015.
    [73] Sridhar, Dhanya and Fakhraei, Shobeir and Getoor, Lise, "A probabilistic approach for collective similarity-based drug--drug interaction prediction," Bioinformatics, vol. 32, pp. 3175--3182, 2016.
    [74] Vilar, Santiago and Uriarte, Eugenio and Santana, Lourdes and Lorberbaum, Tal and Hripcsak, George and Friedman, Carol and Tatonetti, Nicholas P, "Similarity-based modeling in large-scale prediction of drug-drug interactions," Nature protocols, vol. 9, pp. 2147, 2014.
    [75] Zheng, Wei and Lin, Hongfei and Zhao, Zhehuan and Xu, Bo and Zhang, Yijia and Yang, Zhihao and Wang, Jian, "A graph kernel based on context vectors for extracting drug--drug interactions," Journal of biomedical informatics, vol. 61, pp. 34--43, 2016.
    [76] Dai, H.J and, Chang, Y.C and Tsai, R.T.H. and Hsu, W.L.,, "New challenges for biological text-mining in the next decade.," Journal of computer science and technology, vol. 25, pp. 169-179., 2010.
    [77] Abacha and Asma Ben, and Pierre Zweigenbaum, "Automatic extraction of semantic relations between medical entities: a rule based approach," Journal of biomedical semantics, 2011.
    [78] Lee Chew-Hung and Khoo, SG Christopher, and Na, Jin-Cheon, "Automatic identification of treatment relations for medical ontology learning: An exploratory study," 2004.
    [79] Bodenreider, O., "The unified medical language system (UMLS): integrating biomedical terminology," Nucleic acids research, vol. 1, 2004.
    [80] Embarek, Mehdi, and Ferret , Olivier, "Learning Patterns for Building Resources about Semantic Relations in the Medical Domain," Proceedings of the 6th Language Resources and Evaluation Conference, p. 2006–2012, 2008.
    [81] Gopalakrishnan, V and Lustgarten, J. L. and Visweswaran, S., and Cooper, G. F., "Bayesian rule learning for biomedical data mining. Bioinformatics,," vol. 26, pp. 668-675, 2010.
    [82] Hou, Wen-Juan, and Chen, Hsiao-Yuan., "Rule extraction in gene–disease relationship discovery," Gene,, vol. 158, pp. 132-138, 2013.
    [83] Borgelt, Christian, "An Implementation of the FP-growth Algorithm," in roceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, ACM, 2005, pp. 1--5.
    [84] Mitchell, Alex and Kim, Jee-Hyub and Hilario, Melanie and Attwood, Teresa K., "Learning to extract relations for protein annotation," Bioinformatics, vol. 23, pp. i256-i263, 2007.
    [85] Morin, Frederic and Bengio, Yoshua, "Hierarchical probabilistic neural network language model.," in Aistats, vol. 5, Citeseer, 2005, pp. 246--252.
    [86] Willett, P., "The Porter stemming algorithm: then and now," Program, vol. 40, pp. 219--223, 2006.
    [87] Chamberlain, Benjamin Paul and Clough, James and Deisenroth, Marc Peter, "Neural embeddings of graphs in hyperbolic space," arXiv preprint arXiv:1705.10359, 2017.
    [88] Mikolov, Tomas and Sutskever, Ilya and Chen, Kai and Corrado, Greg S and Dean, Jeff, "Distributed representations of words and phrases and their compositionality," in Advances in neural information processing systems, 2013, pp. 3111--3119.
    [89] Goodman, J, "Classes for fast maximum entropy training," arXiv preprint cs/0108006, 2001.
    [90] Saric, Jasmin and Jensen, Lars Juhl and Ouzounova, Rossitza and Rojas, Isabel and Bork, Peer, "Extraction of regulatory gene/protein networks from Medline," Oxford University Press, 2005.
    [91] Simpson, Andrew JR, "Over-Sampling in a Deep Neural Network," arXiv preprint arXiv:1502.03648, 2005.
    [92] Tran, Puc and Mac, Hieu and Tang, Van and Tran, Hai Anh and Nguyen, Linh Giang, "An LSTM based framework for handling multiclass imbalance in DGA botnet detection ," Neurocomputing, vol. 275, pp.2410-2413, 2018.
    [93] Hochreiter, Sepp and Schmidhuber, Jurgen, "Long short-term memory," Neural computation, vol. 9, pp. 1735--1780, 1997.
    [94] LeCun, Yann and Bengio, Yoshua and others, "Convolutional networks for images, speech, and time series," The handbook of brain theory and neural networks, vol. 3361, p. 1995, 1995.
    [95] Williams, Ronald J and Zipser, David, "A learning algorithm for continually running fully recurrent neural networks," Neural computation, vol. 1, pp. 270--280, 1989.
    [96] Sahu, Sunil Kumar and Anand, Ashish, "Drug-drug interaction extraction from biomedical texts using long short-term memory network," Journal of biomedical informatics, vol. 86, pp. 15--24, 2018.
    [97] Suarez-Paniagua, Victor and Segura-Bedmar, Isabel and Martinez, Paloma, "Exploring convolutional neural networks for drug--drug interaction extraction," Database, vol. 2017, 2017.
    [98] Zhang, Yijia and Zheng, Wei and Lin, Hongfei and Wang, Jian and Yang, Zhihao and Dumontier, Michel, "Drug--drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths," Bioinformatics, vol. 34, pp. 828--835, 2017.
    [99] Zhao, Zhehuan and Yang, Zhihao and Luo, Ling and Lin, Hongfei and Wang, Jian, "Drug drug interaction extraction from biomedical literature using syntax convolutional neural network," Bioinformatics, vol. 32, pp. 3444--3453, 2016.
    [100] Abdelaziz, Ibrahim and Fokoue, Achille and Hassanzadeh, Oktie and Zhang, Ping and Sadoghi, Mohammad, "Large-scale structural and textual similarity-based mining of knowledge graph to predict drug--drug interactions," Web Semantics: Science, Services and Agents on the World Wide Web, vol. 44, pp. 104--117, 2017.
    [101] Cao, D-S and Xiao, N and Li, Y-J and Zeng, W-B and Liang, Y-Z and Lu, A-P and Xu, Q-S and Chen, AF, "Integrating multiple evidence sources to predict adverse drug reactions based on a systems pharmacology model," CPT: pharmacometrics and systems pharmacology, vol. 9, pp. 498--506, 2015.
    [102] Ryu, Yong Jaea and Kim, Hyun and Lee, Yup Sang. "Deep learning imporves prediction of drug-drug and drug-food interactions." Proceedings of the National Academy of Sciences, vol. 115, p. 4304--4311, 2018.
    [103] Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J, "Learning representations by back-propagating errors," nature, vol. 323, pp. 533--536, 1986.
    [104] Nielsen, Michael A, Neural networks and deep learning, Determination press San Francisco, CA, USA, 2015.
    [105] Bengio, Yoshua and Simard, Patrice and Frasconi, Paolo and others, "Learning long-term dependencies with gradient descent is difficult," IEEE transactions on neural networks, vol. 5, pp. 157--166, 1994.
    [106] Huang, Degen and Jiang, Zhenchao and Zou, Li and Li, Lishuang, "Drug--drug interaction extraction from biomedical literature using support vector machine and long short term memory networks," Information sciences, vol. 415, pp. 00--109, 2017.
    [107] Al-Rfou, Rami and Choe, Dokook and Constant, Noah and Guo, Mandy and Jones, Llion, "Character-level language modeling with deeper self-attention," in Proceedings of the AAAI Conference on Artificial Intelligence, Vols. 3159--3166, 2019, pp. 3159--3166.
    [108] Dai, Zihang and Yang, Zhilin and Yang, Yiming and Cohen, William W and Carbonell, Jaime and Le, Quoc V and Salakhutdinov, Ruslan, "Transformer-xl: Attentive language models beyond a fixed-length context," arXiv preprint arXiv:1901.02860, 2019.
    [109] Elbayad, Maha and Besacier, Laurent and Verbeek, Jakob, "Pervasive attention: 2d convolutional neural networks for sequence-to-sequence prediction," arXiv preprint arXiv:1808.03867, 2018.
    [110] Hou, Wen-Juan and Ceesay, Bamfa, "Extraction of Drug-drug Interaction Using Neural Embedding," Journal of Bioinformatics and Computational Biology, vol. 16, no. 6, 1840027, 12 2018.
    [111] Boros, Emanuela and Besanccon, Romaric and Ferret, Olivier and Grau, Brigitte, "Event role extraction using domain-relevant word representations," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1852--1857.
    [112] Yakushiji, Akane and Tateisi, Yuka and Miyao, Yusuke and Tsujii, Jun-ichi, "Event extraction from biomedical papers using a full parser," in Biocomputing 2001, World Scientific, 2000, pp. 408--419.
    [113] McDonald, Ryan and Pereira, Fernando and Kulick, Seth and Winters, Scott and Jin, Yang and White, Pete, "Simple Algorithms for Complex Relation Extraction with Applications to Biomedical IE," in Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Stroudsburg, PA, USA, Association for Computational Linguistics, 2005, pp. 491--498.
    [114] Huang, Lifu and Ji, Heng and Cho, Kyunghyun and Voss, Clare R, "Zero-shot transfer learning for event extraction," arXiv preprint arXiv:1707.01066, 2017.
    [115] Hou, Wen Juan and Ceesay, Bamfa, "Event extraction for gene regulation network using syntactic and semantic approaches," in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer, 2015, pp. 559--570.
    [116] Yeh, Alexander and Morgan, Alexander and Colosimo, Marc and Hirschman, Lynette, "BioCreAtIvE task 1A: gene mention finding evaluation," BMC bioinformatics, vol. 6, p. 52, 2005.
    [117] Krallinger, Martin and Leitner, Florian and Valencia, Alfonso, "Assessment of the second BioCreative PPI task: automatic extraction of protein-protein interactions," in Proceedings of the second biocreative challenge evaluation workshop, Madrid, 2007, p. 2007.
    [118] N'edellec, Claire, "Learning language in logic-genic interaction extraction challenge," in Proceedings of the 4th Learning Language in Logic Workshop (LLL05), vol. 7, Citeseer, 2005, pp. 1--7.
    [119] Glorot, Xavier and Bordes, Antoine and Bengio, Yoshua, "Domain adaptation for large-scale sentiment classification: A deep learning approach," in Proceedings of the 28th international conference on machine learning (ICML-11), 2011, pp. 513--520.
    [120] Gopalan, Raghuraman and Li, Ruonan and Chellappa, Rama, "Domain adaptation for object recognition: An unsupervised approach," in 2011 international conference on computer vision, IEEE, 2011, pp. 999--1006.
    [121] Moschitti, Alessandro and Morarescu, Paul and Harabagiu, Sanda M and others, "Open Domain Information Extraction via Automatic Semantic Labeling," in FLAIRS conference, 2003, pp. 397--401.
    [122] Baker, Collin F and Fillmore, Charles J and Lowe, John B, "The berkeley framenet projec," in Proceedings of the 17th international conference on Computational linguistics-Volume 1, Association for Computational Linguistics, 1998, pp. 86--90.
    [123] Li, Fangtao and Pan, Sinno Jialin and Jin, Ou and Yang, Qiang and Zhu, Xiaoyan, "Cross-domain co-extraction of sentiment and topic lexicons," in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, Association for Computational Linguistics, 2012, pp. 410--419.
    [124] Bakker, Bart and Heskes, Tom, "Task clustering and gating for bayesian multitask learning," Journal of Machine Learning Research, vol. 4, pp. 83--99, 2003.
    [125] Kim, Jin-Dong and Pyysalo, Sampo, "BioNLP Shared Task," in Encyclopedia of Systems Biology, Springer New York, 2013, pp. 138--141.
    [126] Pan, Sinno Jialin and Tsang, Ivor W and Kwok, James T and Yang, Qiang, "Domain adaptation via transfer component analysis," IEEE Transactions on Neural Networks, vol. 22, pp. 199--210, 2011.
    [127] Rosipal, Roman and Trejo, Leonard J, "Kernel partial least squares regression in reproducing kernel hilbert space," Journal of machine learning research, vol. 2, pp. 97--123, 2001.
    [128] Palmer, Martha and Gildea, Daniel and Kingsbury, Paul, "The proposition bank: An annotated corpus of semantic roles," Computational linguistics, vol. 31, pp. 71--106, 2005.
    [129] Wold, Svante and Esbensen, Kim and Geladi, Paul, "Principal component analysis," Chemometrics and intelligent laboratory systems, vol. 2, pp. 37-52, 1987.
    [130] Weiss, Karl and Khoshgoftaar, Taghi M and Wang, DingDing, "A survey of transfer learning," Journal of Big Data, vol. 3, p. 9, 2016.
    [131] Shimodaira, Hidetoshi, "Improving predictive inference under covariate shift by weighting the log-likelihood function," Journal of statistical planning and inference, vol. 90, pp. 227-244, 2000.
    [132] Chaix, Estelle and Dubreucq, Bertrand and Fatihi, Abdelhak and Valsamou, Dialekti and Bossy, Robert and Ba, Mouhamadou and Del'eger, Louise and Zweigenbaum and, Pierre and Bessieres, Philippe and Lepiniec, Loic and others, "Overview of the regulatory network of plant seed development (seedev) task at the bionlp shared task 2016," in Proceedings of the 4th BioNLP shared task workshop. Berlin: Association for Computational Linguistic, 2016, pp. 1--11.
    [133] Bossy, Robert and Bessi`eres, Philippe and N'edellec, Claire, "BioNLP shared task 2013--an overview of the genic regulation network task," in Proceedings of the BioNLP Shared Task 2013 Workshop, 2013, pp. 153--160.
    [134] Blitzer, John and McDonald, Ryan and Pereira, Fernando, "Domain adaptation with structural correspondence learning," in Proceedings of the 2006 conference on empirical methods in natural language processing, Association for Computational Linguistics, 2006, pp. 120-128.
    [135] Pan, Sinno Jialin and Yang, Qiang, "A survey on transfer learning," IEEE Transactions on knowledge and data engineering, vol. 22, pp. 1345--1359, 2010.
    [136] Rosipal, Roman and Trejo, Leonard J, "Kernel partial least squares regression in reproducing kernel hilbert space," Journal of machine learning research, vol. 2, pp. 97--123, Dec 2001.
    [137] Argyriou, Andreas and Maurer, Andreas and Pontil, Massimiliano, "An algorithm for transfer learning in a heterogeneous environment," in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, 2008, pp. 71--85.
    [138] Pan, Sinno Jialin and Yang, Qiang, "A survey on transfer learning," IEEE Transactions on knowledge and data engineering, vol. 22, pp. 1345--1359, 2010.
    [139] B.-S. 2013, "Gene Regulation Network (GRN)," BioNLP-ST 2013, 30 09 2013. [Online]. Available: http://2013.bionlp-st.org/tasks/gene-regulation-network.
    [140] T. Joachims, Learning to classify text using support vector machines: Methods, theory and algorithms, vol. 186, Kluwer Academic Publishers Norwell, 2002.
    [141] Hochreiter, Sepp and Schmidhuber, J{\"u}rgen, "Long short-term memory," Neural computation, vol. 9, pp. 1735--1780, 1997.

    無法下載圖示 本全文未授權公開
    QR CODE