Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction

• URL: http://arxiv.org/abs/2004.06216v1
• Date: Mon, 13 Apr 2020 22:01:38 GMT
• Title: Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction
• Authors: Hong Guan, Jianfu Li, Hua Xu, Murthy Devarakonda
• Abstract summary: We studied several variants of BERT (Bidirectional Representations using Transformers) We evaluated these methods using a direct temporal relations dataset which is a semantically focused subset of the 2012 i2b2 temporal relations challenge dataset. Results: RoBERTa, which employs better pre-training strategies including using 10x larger corpus, has improved overall F measure by 0.0864 absolute score (on the 1.00 scale) and thus reducing the error rate by 24% relative to the previous state-of-the-art performance achieved with an SVM (support vector machine) model.
• Score: 10.832917897850361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Methods: We studied several variants of BERT (Bidirectional Encoder Representations using Transformers) some involving clinical domain customization and the others involving improved architecture and/or training strategies. We evaluated these methods using a direct temporal relations dataset which is a semantically focused subset of the 2012 i2b2 temporal relations challenge dataset. Results: Our results show that RoBERTa, which employs better pre-training strategies including using 10x larger corpus, has improved overall F measure by 0.0864 absolute score (on the 1.00 scale) and thus reducing the error rate by 24% relative to the previous state-of-the-art performance achieved with an SVM (support vector machine) model. Conclusion: Modern contextual language modeling neural networks, pre-trained on a large corpus, achieve impressive performance even on highly-nuanced clinical temporal relation tasks.

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