【论文解读 EMNLP 2018 | JMEE】Jointly Multiple EE via Attention-based Graph Information Aggregation

时间 2021-01-09

标签论文 NE&GNN 事件抽取 EMNLP GCN 自然语言处理繁體版

原文原文链接

论文题目：Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation 论文来源：EMNLP 2018 论文链接：https://arxiv.org/abs/1809.09078 代码链接：https://github.com/lx865712528/EMNLP2018-JMEE 关键词：多

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1. 【论文解读 EMNLP 2019 | MOGANED】ED with Multi-Order Graph Convolution and Aggregated Attention
2. Information Aggregation via Dynamic Routing for Sequence Encoding
3. 【论文解读 EMNLP 2018】Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content
4. 【论文解读 WWW 2018 | GraphInception】Deep Collective Classification in Heterogeneous Information Networks
5. 论文笔记&总结|JMEE事件抽取联合模型
6. Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks论文解读
7. 论文导读｜《Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model》
8. 【论文解读 CIKM 2018 | GEM】Heterogeneous Graph Neural Networks for Malicious Account Detection
9. 论文解读《Collective Entity Alignment via Adaptive Features》
10. 论文笔记：LCLR 2018 FastGCN: Fast Learning With Graph Convolutional Networks Via Important Sampling

>>更多相关文章<<