Cascade Decoding for Antibiotic Resistance Event Extraction Based on Contrastive Learning.

Antibiotic resistance event extraction involves the automated extraction of information related to antibiotic resistance mechanisms from a vast amount of biomedical literature. This can be achieved by utilizing natural language processing techniques. However, the distinctive characteristics of the biomedical field lead to various challenges for existing antibiotic resistance event extraction methods, such as limited labeling data, complex names of biomedical entities, and nesting and overlapping event structures. These factors make it challenging to apply the current processing methods for biomedical text to the task of antibiotic resistance event extraction. To address these challenges, we propose a cascade decoding approach for antibiotic resistance event extraction based on contrastive learning (CL-MA-CasEE). This approach achieves data augmentation by constructing two contrastive learning tasks, which combines entity type embedding and POS embedding to enrich the semantic information of word representations. Furthermore, it performs event type detection, event trigger extraction, and event argument extraction through using three cascade decoders to simulate the complex event structures. Based on experiments, we demonstrate that our method can effectively extract structured antibiotic resistance event information from biomedical literature, thereby improve the efficiency of event extraction tasks as well.