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• W2943040665 abstract "Motivation: Biomedical event detection is fundamental for information extraction in molecular biology and biomedical research. The detected events form the central basis for comprehensive biomedical knowledge fusion, facilitating the digestion of massive information influx from literature. Limited by the feature context, the existing event detection models are mostly applicable for a single task. A general and scalable computational model is desiderated for biomedical knowledge management. Results: We consider and propose a bottom-up detection framework to identify the events from recognized arguments. To capture the relations between the arguments, we trained a bi-directional Long Short-Term Memory (LSTM) network to model their context embedding. Leveraging the compositional attributes, we further derived the candidate samples for training event classifiers. We built our models on the datasets from BioNLP Shared Task for evaluations. Our method achieved the average F-scores of 0.81 and 0.92 on BioNLPST-BGI and BioNLPST-BB datasets respectively. Comparing with 7 state-of-the-art methods, our method nearly doubled the existing F-score performance (0.92 vs 0.56) on the BioNLPST-BB dataset. Case studies were conducted to reveal the underlying reasons. Availability: https://github.com/cskyan/evntextrc" @default.
• W2943040665 created "2019-05-09" @default.
• W2943040665 creator A5016741865 @default.
• W2943040665 creator A5058256601 @default.
• W2943040665 date "2019-05-02" @default.
• W2943040665 modified "2023-10-14" @default.
• W2943040665 title "Context awareness and embedding for biomedical event extraction" @default.
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• W2943040665 doi "https://doi.org/10.48550/arxiv.1905.00982" @default.
• W2943040665 hasPublicationYear "2019" @default.
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