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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:143下載:0
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  • 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

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