Hierarchical Distillation Network for Biomedical Event Extraction
Lishuang Li, Mengzuo Huang, Beibei Zhang · 2020
Biomedical event extraction is a challenging task in biomedical information extraction. There exist two main problems in previous works: (1) Existing methods are insufficient to capture the indicative information from distant context. (2) Existing methods are not skilled at generating the hierarchical representations for each sentence, which is important for the downstream classification task. In this paper, we propose a novel Hierarchical Distillation Network (HDN) for biomedical event extraction. Firstly, HDN encodes a given sentence from multiple perspectives: a bidirectional GRU (BI-GRU) is employed for sequential encoding and several graph convolution networks (GCN) are employed for multi-order syntactic encodings. Second, HDN integrates the distillation module which can emphasize the difference among all levels of encodings to reduce the redundant information, then HDN adopts residual connection to obtain more expressive sentence representation. Finally, we perform biomedical event extraction on the commonly used Multi-Level Event Extraction (MLEE) corpus and achieve an F1-score of 62.74% which is 3.13% higher than the previous best.