Low-Resource Biomedical Event Detection Based on Semantic Matching and Meta Learning

Biomedical event detection is a critical task that seeks to identify event triggers of certain types in texts. Most existing methods focus on supervised learning schemes, which require a large amount of annotated data and cannot generalize to rare or unseen event types that emerge with only a few or no annotated data available. Existing research on low-resource biomedical event detection has only been conducted in few-shot scenarios, and cannot simultaneously detect rare and unseen event types. In addition, previous work has focused on detecting flat events, neglecting the detection of overlapping events. To address the above problems, we propose LRBED, a unified meta learning framework for both few- and zero-shot biomedical event detection, which can learn from seen event types, yet with the meta objective to generalize in rare and unseen event types. To tackle the detection issues of flat and overlapping events, in our framework, we further propose a novel event-aware semantic matching model that uses event types as semantically rich event-aware prompts to extract candidate triggers from the input text. With our designed joint learning meta objective based on contrastive learning and attention mechanism, our model can better capture the semantic relevance between event-aware prompts and potential trigger words in the input text. In this way, an overlapping event will be combined with all event-aware prompts, which can match all its corresponding event types, solving the problem of flat and overlapping event detection in low-resource scenarios. Experiments on the benchmark datasets demonstrate the effectiveness of LRBED in both few-shot and zero-shot scenarios, and LRBED outperforms the existing method in the same few-shot scenarios. In addition, we also validate the ability of our model to detect overlapping events in low-resource scenarios.