Machine learning based extraction of semantic relations from biomedical literature

VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY LE HOANG QUYNH MACHINE LEARNING-BASED EXTRACTION OF SEMANTIC RELATIONS FROM BIOMEDICAL LITERATURE DOCTOR OF PHILOSOPHY IN INFORMATION TECHNOLOGY DISSERTATION Hanoi, 2022 VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY LE HOANG QUYNH MACHINE LEARNING-BASED EXTRACTION OF SEMANTIC RELATIONS FROM BIOMEDICAL LITERATURE Major: Information Systems Code: 9480104.01 DOCTOR OF PHILOSOPHY IN INFORMATION TECHNOLOGY DISSERTATION SUPERVISORS: Prof Dr Nigel Collier Dr Dang Thanh Hai Hanoi, 2022 Declaration I hereby declare that this Doctoral Dissertation was carried out by me for the degree of Doctor of Philosophy under the guidance and supervision of my supervisors This dissertation is my own work and includes nothing, which is the outcome of work done in collaboration except as specified in the text It is not substantially the same as any I have submitted for a degree, diploma or other qualification at any other university; and no part has already been, or is currently being submitted for any degree, diploma or other qualification Hanoi , January 2022 Author Le Hoang Quynh iii Table of Contents DECLARATION iii TABLE OF CONTENTS iv ABBREVIATIONS viii LIST OF FIGURES xi LIST OF TABLES xiii PREFACE 1 INTRODUCTION TO BIOMEDICAL RELATION EXTRACTION 11 1.1 Problem statement 11 1.1.1 Semantic relation extraction 11 1.1.2 Biomedical named entity recognition 12 1.1.3 Biomedical relation classification 15 1.2 Literature review 19 1.2.1 Literature review of biomedical named entity recognition 19 1.2.2 Literature review of biomedical relation extraction 24 1.2.3 Related doctoral dissertations 29 1.3 Related resources 30 1.3.1 Datasets for named entity recognition experiments 31 1.3.2 Datasets for relation classification experiments 32 1.4 Evaluation metrics 34 1.4.1 Evaluation metrics 34 1.4.2 Named entity recognition evaluation 35 1.4.3 Relation classification evaluation 36 1.5 Summary 37 iv AN END-TO-END PIPELINE MODEL FOR BIOMEDICAL RELATION EXTRACTION 38 2.1 Distant supervision learning with silverCID corpus 39 2.2 Proposed UET-CAM system 42 2.2.1 Joint model of named entity recognition and normalization (DNER) 43 2.2.2 Coreference resolution 49 2.2.3 Intra-sentence relation classification with support vector machine 52 2.3 Experimental results and discussion 54 2.3.1 Choosing the combining manner of SSI and skip-gram for named entity normalization results 54 2.3.2 Named entity recognition and normalization results 55 2.3.3 CID relation classification results 57 2.3.4 Discussion 58 2.4 Summary 62 AN IMPROVED CRF-BILSTM MODEL FOR BIOMEDICAL NAMED ENTITY RECOGNITION 64 3.1 Introduction to deep learning for named entity recognition 65 3.2 Proposed D3NER model 67 3.2.1 Data pre-processing 67 3.2.2 The TPAC embeddings layer 68 3.2.3 Context representing biLSTM layer 71 3.2.4 Project layer 72 3.2.5 Conditional random fields layer 72 3.3 Experimental results and discussion 72 3.3.1 Experimental environment and model settings 73 3.3.2 Comparative models 75 3.3.3 The performance of D3NER model and comparisons 76 3.3.4 Contribution of the model components 80 3.3.5 Error analysis 82 3.4 Summary 86 HYBRID, ATTENTION-BASED AND ENSEMBLE DEEP LEARNING MODELS FOR BIOMEDICAL RELATION CLASSIFICATION 87 4.1 The shortest dependency path 89 4.1.1 Dependency tree 89 v 4.1.2 The shortest dependency path 90 4.1.3 Dependency Unit 91 4.2 A hybrid adaptive deep learning model for biomedical relation extraction 91 4.2.1 Proposed MASS model 92 4.2.2 Experimental corpora and comparative models 98 4.2.3 Experimental environment and model settings 100 4.2.4 Experimental results and discussion 100 4.3 An attentive augmented deep learning model for biomedical relation extraction 106 4.3.1 Richer-but-smarter SDP 106 4.3.2 Proposed RbSP model 107 4.3.3 Experimental environment and model settings 114 4.3.4 Experimental results and discussion 114 4.4 A multi-fragment ensemble deep learning model for biomedical relation extraction 118 4.4.1 Over-fitting problem of deep learning-based models 118 4.4.2 Bagging with bootstrap training data 119 4.4.3 Proposed multi-fragment ensemble architecture 121 4.4.4 Experimental results and discussion 124 4.5 Summary 129 GRAPH-BASED INTER-SENTENCE RELATION CLASSIFICATION IN BIOMEDICAL TEXT 131 5.1 Inter-sentence relations classification problem 132 5.2 Proposed graph-based inter-sentence relation classification model 134 5.2.1 Model overview 134 5.2.2 Document sub-graph construction 135 5.2.3 Paths finding, merging and choosing 138 5.2.4 Shared-weight convolutional neural network 140 5.3 Experimental results and discussion 143 5.3.1 Experimental environment and model settings 143 5.3.2 Contribution of the added virtual edges in document sub-graph 144 5.3.3 Different sliding window size w for training and testing 145 5.3.4 Contribution of the model components 146 5.3.5 Comparison to comparative model 148 5.4 Discussion 150 vi 5.5 Summary 152 CONCLUSION 156 LIST OF PUBLICATIONS 158 BIBLIOGRAPHY 158 vii ABBREVIATIONS Acc Accuracy Adam Adaptive Moment Estimation ANN Artificial Neural Network bagging Bootstrap Aggregating BB3 Bacteria Biotope Task BC5 CDR corpus BioCreative V Chemical-Disease relation corpus BERT Bidirectional Encoder Representations from Transformers biLSTM Bidirectional Long Short-term Memory CBOW Continuous Bag-of-words CDR Chemical Disease Relation CID Chemical-induced Disease CNN Convolutional Neural Network CRF Conditional Random Fields CTD Comparative Toxicogenomics Database DDI Drug-drug Interaction DNER Disease Named Entity Recognition DNN Deep Neural Network DU Dependency Unit ELMO Embeddings from Language Models FN False Negative viii FP False Positive FSU-PRGE The FSU PRotein GEne Corpus GD Gradient Descent HAScO Human-Aware Science Ontology HHEAR Human Health Exposure Analysis Resource HMM Hidden Markov Model IAA Inter-annotator Agreement IE Information Extraction KB Knowledge-base LSTM Long Short-term Memory MASS Man for All SeasonS MESH Medical Subject Headings mf Multi-fragment MLP Multilayer Perceptron MUC Message Understanding Conferences NCBI National Center for Biotechnology Information NCIT National Cancer Institute Thesaurus NE Named Entity NEN Named Entity Normalization NER Named Entity Recognition NLP Natural Language Processing OOV Out-Of-Vocabulary OWL Orthology Ontology P Precision PMC Pubmed Central ix POS Part-of-speech R Recall RbSP Richer-but-Smarter Shortest Dependency Path RC Relation Classification RE Relation Extraction ReLU Rectified Linear Unit REP Replacement RGO Radiology Gamuts Ontology RNN Recurrent Neural Network SDP The Shortest Dependency Path SilverCID A Silver-standard Corpus for Chemicalinduced Disease Relation Extraction SNOMED Systematized Nomenclature of Medicine SSI Supervised Semantic Indexing stdev Standard Deviation SVM Suport Vector Machine swCNN Shared-weight Convolutional Neural Network TN True 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UNIVERSITY OF ENGINEERING AND TECHNOLOGY LE HOANG QUYNH MACHINE LEARNING-BASED EXTRACTION OF SEMANTIC RELATIONS FROM BIOMEDICAL LITERATURE Major: Information Systems Code: 9480104.01 DOCTOR OF PHILOSOPHY... extraction 1.1.1 Semantic relation extraction First of all, we present the definition of semantic relation extraction in Definition 1.1 Definition 1.1 Semantic relations (or semantic relationships)... 19 1.2.1 Literature review of biomedical named entity recognition 19 1.2.2 Literature review of biomedical relation extraction 24 1.2.3 Related doctoral