Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction

13 Apr 2020 Hong Guan Jianfu Li Hua Xu Murthy Devarakonda

Background: Identifying relationships between clinical events and temporal expressions is a key challenge in meaningfully analyzing clinical text for use in advanced AI applications. While previous studies exist, the state-of-the-art performance has significant room for improvement... (read more)

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Methods used in the Paper


METHOD TYPE
RoBERTa
Transformers
SVM
Non-Parametric Classification
Residual Connection
Skip Connections
Attention Dropout
Regularization
Linear Warmup With Linear Decay
Learning Rate Schedules
Weight Decay
Regularization
GELU
Activation Functions
Dense Connections
Feedforward Networks
Adam
Stochastic Optimization
WordPiece
Subword Segmentation
Softmax
Output Functions
Dropout
Regularization
Multi-Head Attention
Attention Modules
Layer Normalization
Normalization
Scaled Dot-Product Attention
Attention Mechanisms
BERT
Language Models