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        <title>Biomedical Event Extraction with Hierarchical Knowledge Graphs</title>
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        <namePart type="given">Kung-Hsiang</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.114</identifier>
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%0 Conference Proceedings
%T Biomedical Event Extraction with Hierarchical Knowledge Graphs
%A Huang, Kung-Hsiang
%A Yang, Mu
%A Peng, Nanyun
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F huang-etal-2020-biomedical
%X 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.
%R 10.18653/v1/2020.findings-emnlp.114
%U https://aclanthology.org/2020.findings-emnlp.114
%U https://doi.org/10.18653/v1/2020.findings-emnlp.114
%P 1277-1285

Markdown (Informal)

[Biomedical Event Extraction with Hierarchical Knowledge Graphs](https://aclanthology.org/2020.findings-emnlp.114) (Huang et al., Findings 2020)

• Biomedical Event Extraction with Hierarchical Knowledge Graphs (Huang et al., Findings 2020)

ACL

• Kung-Hsiang Huang, Mu Yang, and Nanyun Peng. 2020. Biomedical Event Extraction with Hierarchical Knowledge Graphs. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1277–1285, Online. Association for Computational Linguistics.