Name
Title | ||
|---|---|---|
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing |
Abstract | ||
|---|---|---|
In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance. To this end, we propose to exploit sibling mentions for enhancing the mention representations. Specifically, we present two different metrics for sibling selection and employ an attentive graph neural network to aggregate information from sibling mentions. The proposed graph model is scalable in that unseen test mentions are allowed to be added as new nodes for inference. Exhaustive experiments demonstrate the effectiveness of our sibling learning strategy, where our model outperforms ten strong baselines. Moreover, our experiments indeed prove the superiority of sibling mentions in helping clarify the types for hard mentions. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.18653/v1/2022.acl-long.147 | PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS) |
DocType | Volume | Citations |
Conference | Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Chen | 1 | 0 | 0.34 |
| Jiayang Cheng | 2 | 0 | 0.34 |
| Haiyun Jiang | 3 | 0 | 1.01 |
| Lemao Liu | 4 | 87 | 18.74 |
| Haisong Zhang | 5 | 15 | 8.00 |
| Shuming Shi | 6 | 620 | 58.27 |
| Xu Ruifeng | 7 | 432 | 53.04 |