Ensemble Learning with Pre-Trained Transformers for Crash Severity Classification: A Deep NLP Approach

Keyphrases

• Deep NLP 100%
• Crash Severity 100%
• Generative Transformer 100%
• Term Frequency-inverse Document Frequency (TF-IDF) 100%
• Ensemble Learning 100%
• Severity Classification 100%
• Word2vec 75%
• Augmented Dataset 75%
• Property Damage 50%
• RoBERTa 50%
• Fatal Injuries 50%
• Word Embedding 50%
• Personal Injury 50%
• Ensemble Model 50%
• Pre-trained Language Model 50%
• Textual Classification 25%
• Majority Class 25%
• Voting Classifier 25%
• Machine Learning Learning 25%
• Three-machine 25%
• Crash Data 25%
• Motorists 25%
• Missouri 25%
• Imbalanced Data 25%
• Classification Model 25%
• Comprehensive Experiment 25%
• Deep Machine Learning 25%
• Data Augmentation 25%
• Minority Class 25%
• Bidirectional Long Short-term Memory (BiLSTM) 25%
• Natural Language Processing 25%
• Transfer Learning 25%
• Feature Extraction Methods 25%
• State Highway 25%
• Naïve Bayes 25%
• Damage Classification 25%
• Highway Patrol 25%
• Deep Learning Model 25%
• State Emergence 25%
• Conventional Techniques 25%
• Hard Voting 25%
• Highly Effective 25%

Computer Science

• Bidirectional Encoder Representations From Transformers 100%
• Ensemble Learning 100%
• Word Embedding 50%
• Machine Learning 50%
• Learning System 50%
• Pre-Trained Language Models 50%
• Feature Extraction 25%
• Bidirectional Long Short-Term Memory Network 25%
• Data Augmentation 25%
• Individual Model 25%
• Majority Class 25%
• Transfer Learning 25%
• Classification Models 25%
• Random Decision Forest 25%
• Minority Class 25%
• Embedding Method 25%
• Natural Language Processing 25%
• Context Dependent 25%
• Deep Learning Model 25%