Automatic Large-scale Data Generation for Open-topic Biomedical Event Relation Extraction

Biomedical event relation extraction (BioERE) plays an important role in many downstream biological applications. Recent efforts based on supervised learning from small hand-labeled data usually suffer from low coverage of relation topics and limited scale. These shortages make supervised methods hard to achieve competitive performances and predict unseen relations in other topics. Thus, we explore the open-topic BioERE task to simultaneously extract event relations of multiple topics based on the automatically labeled large-scale training data via detecting key meta paths using distant supervision. The experimental results show that the quality of the generated data is competitive to enhance the performances of the open-topic BioERE models.