A tree-like structured perceptron for transition-based biomedical event extraction

Su, Fangfang; Qian, Tao; Zhou, Jun; Li, Bobo; Li, Fei; Teng, Chong; Ji, Donghong

doi:10.1016/j.knosys.2023.111180

Knowledge-Based Systems

2024

DOI: 10.1016/j.knosys.2023.111180

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A tree-like structured perceptron for transition-based biomedical event extraction

Fangfang Su,

Tao Qian,

Jun Zhou

et al.

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Cited by 3 publications

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Prompt for extraction: Multiple templates choice model for event extraction

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Prompt for extraction: Multiple templates choice model for event extraction

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Multimodal Attention-Based Instruction-Following Part-Level Affordance Grounding

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The integration of language and vision for object affordance understanding is pivotal for the advancement of embodied agents. Current approaches are often limited by reliance on segregated pre-processing stages for language interpretation and object localization, leading to inefficiencies and error propagation in affordance segmentation. To overcome these limitations, this study introduces a unique task, part-level affordance grounding, in direct response to natural language instructions. We present the Instruction-based Affordance Grounding Network (IAG-Net), a novel architecture that unifies language–vision interactions through a varied-scale multimodal attention mechanism. Unlike existing models, IAG-Net employs two textual–visual feature fusion strategies, capturing both sentence-level and task-specific textual features alongside multiscale visual features for precise and efficient affordance prediction. Our evaluation on two newly constructed vision–language affordance datasets, ITT-AFF VL and UMD VL, demonstrates a significant leap in performance, with an improvement of 11.78% and 0.42% in mean Intersection over Union (mIoU) over cascaded models, bolstering both accuracy and processing speed. We contribute to the research community by releasing our source code and datasets, fostering further innovation and replication of our findings.

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A tree-like structured perceptron for transition-based biomedical event extraction

Cited by 3 publications

References 44 publications

Prompt for extraction: Multiple templates choice model for event extraction

Prompt for extraction: Multiple templates choice model for event extraction

Towards document-level event extraction via Binary Contrastive Generation

Multimodal Attention-Based Instruction-Following Part-Level Affordance Grounding

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