Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via Graph Edge-conditioned Attention Networks (GEANet) and hierarchical graph representation. To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and 3.19% F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.
@article{arxiv.2009.09335,
title = {Biomedical Event Extraction with Hierarchical Knowledge Graphs},
author = {Kung-Hsiang Huang and Mu Yang and Nanyun Peng},
journal= {arXiv preprint arXiv:2009.09335},
year = {2020}
}
Comments
8 pages, 3 figures, Findings of EMNLP 2020 (short)