GENE: Global Event Network Embedding
Qi Zeng, Manling Li, Tuan Lai, Heng Ji, Mohit Bansal, Hanghang Tong
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.- Anthology ID:
- 2021.textgraphs-1.5
- Volume:
- Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
- Month:
- June
- Year:
- 2021
- Address:
- Mexico City, Mexico
- Editors:
- Alexander Panchenko, Fragkiskos D. Malliaros, Varvara Logacheva, Abhik Jana, Dmitry Ustalov, Peter Jansen
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 42–53
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2021.textgraphs-1.5/
- DOI:
- 10.18653/v1/2021.textgraphs-1.5
- Cite (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.
- Cite (Informal):
- GENE: Global Event Network Embedding (Zeng et al., TextGraphs 2021)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2021.textgraphs-1.5.pdf
- Code
- pkuzengqi/gene