@inproceedings{zeng-etal-2021-gene,
title = "{GENE}: Global Event Network Embedding",
author = "Zeng, Qi and
Li, Manling and
Lai, Tuan and
Ji, Heng and
Bansal, Mohit and
Tong, Hanghang",
editor = "Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Logacheva, Varvara and
Jana, Abhik and
Ustalov, Dmitry and
Jansen, Peter",
booktitle = "Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.textgraphs-1.5",
doi = "10.18653/v1/2021.textgraphs-1.5",
pages = "42--53",
abstract = "Current methods for event representation ignore related events in a corpus-level global context. For a deep and comprehensive understanding of complex events, we introduce a new task, Event Network Embedding, which aims to represent events by capturing the connections among events. We propose a novel framework, Global Event Network Embedding (GENE), that encodes the event network with a multi-view graph encoder while preserving the graph topology and node semantics. The graph encoder is trained by minimizing both structural and semantic losses. We develop a new series of structured probing tasks, and show that our approach effectively outperforms baseline models on node typing, argument role classification, and event coreference resolution.",
}
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%0 Conference Proceedings
%T GENE: Global Event Network Embedding
%A Zeng, Qi
%A Li, Manling
%A Lai, Tuan
%A Ji, Heng
%A Bansal, Mohit
%A Tong, Hanghang
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Logacheva, Varvara
%Y Jana, Abhik
%Y Ustalov, Dmitry
%Y Jansen, Peter
%S Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zeng-etal-2021-gene
%X Current methods for event representation ignore related events in a corpus-level global context. For a deep and comprehensive understanding of complex events, we introduce a new task, Event Network Embedding, which aims to represent events by capturing the connections among events. We propose a novel framework, Global Event Network Embedding (GENE), that encodes the event network with a multi-view graph encoder while preserving the graph topology and node semantics. The graph encoder is trained by minimizing both structural and semantic losses. We develop a new series of structured probing tasks, and show that our approach effectively outperforms baseline models on node typing, argument role classification, and event coreference resolution.
%R 10.18653/v1/2021.textgraphs-1.5
%U https://aclanthology.org/2021.textgraphs-1.5
%U https://doi.org/10.18653/v1/2021.textgraphs-1.5
%P 42-53
Markdown (Informal)
[GENE: Global Event Network Embedding](https://aclanthology.org/2021.textgraphs-1.5) (Zeng et al., TextGraphs 2021)
ACL
- Qi Zeng, Manling Li, Tuan Lai, Heng Ji, Mohit Bansal, and Hanghang Tong. 2021. GENE: Global Event Network Embedding. In Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15), pages 42–53, Mexico City, Mexico. Association for Computational Linguistics.