Biomedical Event Extraction with Hierarchical Knowledge Graphs

• URL: http://arxiv.org/abs/2009.09335v3
• Date: Mon, 12 Oct 2020 16:38:31 GMT
• Title: Biomedical Event Extraction with Hierarchical Knowledge Graphs
• Authors: Kung-Hsiang Huang, Mu Yang, Nanyun Peng
• Abstract summary: 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. 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.
• Score: 34.078835099079285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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