Automatic Cause-Effect Relation Extraction from Dental Textbooks Using BERT

• Main Authors: Terapat Chansai, Ruksit Rojpaisarnkit, Teerakarn Boriboonsub, Suppawong Tuarob, Myat Su Yin, Peter Haddawy, Saeed Ul Hassan, Mihai Pomarlan
• Other Authors: Manchester Metropolitan University
• Format: Conference or Workshop Item
• Published: 2022
• Subjects: Computer Science; Mathematics
• Online Access: https://repository.li.mahidol.ac.th/handle/123456789/76725
• Institution: Mahidol University

The ability to automatically identify causal relations from surgical textbooks could prove helpful in the automatic construction of ontologies for dentistry and building learning-assistant tools for dental students where questions about essential concepts can be auto-generated from the extracted ontologies. In this paper, we propose a neural network architecture to extract cause-effect relations from dental surgery textbooks. The architecture uses a transformer to capture complex causal sentences, specific semantics, and large-scale ontologies and solve sequence-to-sequence tasks while preserving long-range dependencies. Furthermore, we have also used BERT to learn word contextual relations. During pre-training, BERT is trained on enormous corpora of unannotated text on the web. These pre-trained models can be fine-tuned on custom tasks with specific datasets. We first detect sentences that contain cause-effect relations. Then, cause and effect clauses from each cause-effect sentence are identified and extracted. Both automatic and expert-rated evaluations are used to validate the efficacy of our proposed models. Finally, we discuss a prototype system that helps dental students learn important concepts from dental surgery textbooks, along with our future research directions.