Terapat ChansaiRuksit RojpaisarnkitTeerakarn BoriboonsubSuppawong TuarobMyat Su YinPeter HaddawySaeed Ul HassanMihai PomarlanManchester Metropolitan UniversityMahidol UniversityUniversität Bremen2022-08-042022-08-042021-01-01Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.13133 LNCS, (2021), 127-13816113349030297432-s2.0-85121912766https://repository.li.mahidol.ac.th/handle/20.500.14594/76725The 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.Mahidol UniversityComputer ScienceMathematicsConference PaperSCOPUS10.1007/978-3-030-91669-5_11