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- W4385010908 abstract "The investigation of new associations between genes and diseases is a crucial knowledge extraction task for drug discovery. In this paper, we present a novel approach to gene-disease knowledge discovery in the 8th China Health Information Processing Conference (CHIP 2022) [31] Open Shared Task AGAC Track ( http://www.cips-chip.org.cn/2022/eval1 ). Selective annotation and latent topic annotation are two major challenges in this task. To address these challenges, we propose a novel Hierarchical Global Pointer Network with propagation module to extract implicit relations that are inferred based on explicit relations. We are also the first one to design a unified end-to-end model which can achieve three AGAC tasks simultaneously and alleviate the problem of selective annotation. The experiment results show the method we proposed can achieve F1-scores of 52%, 31% and 30% for three tasks respectively." @default.
- W4385010908 created "2023-07-22" @default.
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- W4385010908 date "2023-01-01" @default.
- W4385010908 modified "2023-09-24" @default.
- W4385010908 title "Hierarchical Global Pointer Network: An Implicit Relation Inference Method for Gene-Disease Knowledge Discovery" @default.
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- W4385010908 doi "https://doi.org/10.1007/978-981-99-4826-0_2" @default.
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