Abstract
Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especially for in-domain scenarios. To address this limitation, in this paper, we propose attentive graph convolutional networks (A-GCN) to improve neural RE methods with an unsupervised manner to build the context graph, without relying on the existence of a dependency parser. Specifically, we construct the graph from n-grams extracted from a lexicon built from pointwise mutual information (PMI) and apply attention over the graph. Therefore, different word pairs from the contexts within and across n-grams are weighted in the model and facilitate RE accordingly. Experimental results with further analyses on two English benchmark datasets for RE demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on both datasets.- Anthology ID:
- 2021.emnlp-main.228
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2860–2868
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.228
- DOI:
- 10.18653/v1/2021.emnlp-main.228
- Cite (ACL):
- Han Qin, Yuanhe Tian, and Yan Song. 2021. Relation Extraction with Word Graphs from N-grams. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2860–2868, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Relation Extraction with Word Graphs from N-grams (Qin et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2021.emnlp-main.228.pdf
- Data
- SemEval-2010 Task-8