Abstract
Entity and Relation Extraction (ERE) is an important task in information extraction. Recent marker-based pipeline models achieve state-of-the-art performance, but still suffer from the error propagation issue. Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial.In this work, we propose HyperGraph neural network for ERE (HGERE), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model). To alleviate error propagation, we use a high-recall pruner mechanism to transfer the burden of entity identification and labeling from the NER module to the joint module of our model. For higher-order modeling, we build a hypergraph, where nodes are entities (provided by the span pruner) and relations thereof, and hyperedges encode interactions between two different relations or between a relation and its associated subject and object entities. We then run a hypergraph neural network for higher-order inference by applying message passing over the built hypergraph. Experiments on three widely used benchmarks (ACE2004, ACE2005 and SciERC) for ERE task show significant improvements over the previous state-of-the-art PL-marker.- Anthology ID:
- 2023.emnlp-main.467
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7512–7526
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.467
- DOI:
- 10.18653/v1/2023.emnlp-main.467
- Cite (ACL):
- Zhaohui Yan, Songlin Yang, Wei Liu, and Kewei Tu. 2023. Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7512–7526, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks (Yan et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2023.emnlp-main.467.pdf