Knowledge-aware Few-shot Learning Framework for Biomedical Event Trigger Identification
Shujuan Yin, Weizhong Zhao, Xingpeng Jiang, Tingting He · 2020
Biomedical event extraction aims to detect fine-grained interactions between biomedical entities in biomedical texts, and has become a research hotspot for researchers in the biomedical field. As the first step in biomedical event extraction, biomedical event trigger identification plays an important role in the whole process. Although researchers have proposed many methods, the performance of existing methods is not desirable due to the reliance on a large number of labeled training samples and the need for expert knowledge in the biomedical field. To treat this issue, we model the biomedical event trigger identification as a few-shot learning problem. Specifically, we utilize a knowledge-aware attention layer to obtain a rich informative representation for entities, and improve the derived prototypes accordingly by prototypical network. In addition, the module of relation network is introduced to train a more reasonable distance function for trigger type prediction. The results demonstrate the effectiveness of the proposed framework according to F1-score.