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Edge-Enhanced Graph Convolution Networks for Event ...
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由 S Cui 著作2020被引用 129 次 — We propose the novel EE-GCN that simultane- ously integrate syntactic structure and typed dependency labels to improve neural event detection, and learns to ...
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Edge-Enhanced Graph Convolution Networks for Event ...
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由 S Cui 著作2020被引用 129 次 — Abstract. Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text.
Edge-Enhanced Graph Convolution Networks for Event ...
arXiv
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由 S Cui 著作2020被引用 129 次 — In this paper, we propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN), which simultaneously exploits syntactic structure and ...
Edge-Enhanced Graph Convolution Networks for Event ...
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Cui et al. [16] proposed the edge-enhanced graph convolutional networks that utilize both syntactic structure and type label information for event detection.
论文笔记EMNLP 2020|Edge-Enhanced Graph Convolution ...
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2021年11月22日 — 1 简介. 论文题目：Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation 论文来源：EMNLP 2020
cuishiyao96/eegcned
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This code is for Findings of EMNLP2020 paper Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation. Event detection (ED) ...
Papers Related To "Edge-Enhanced Graph Convolution ...
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Highlight: For this reason, this paper proposes a new method for event detection, which uses a dependency tree based graph convolution network with aggregative ...
arXiv:2002.10757v2 [cs.CL] 29 Sep 2020
arXiv
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由 S Cui 著作2020被引用 129 次 — In this paper, we propose a novel architecture named Edge-. Enhanced Graph Convolution Networks (EE-. GCN), which simultaneously exploits syntac ...
Event detection with multi-order edge-aware graph ...
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由 J Wang 著作2023被引用 8 次 — In this paper, we propose a multi-order edge-aware graph convolution network (MEA-GCN) based on multi-order grammar and typed dependent label information.
Edge Enhanced Graph Convolutional Neural Network ...
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由 F Niu 著作 — Graph convolutional networks (GCN) have shown impressive performance in various fields of research, especially in node classification tasks.