Papers with EDL
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition (2023.findings-acl)
Copied to clipboard
Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu
| Challenge: | Named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty. |
| Approach: | They propose to introduce two uncertainty-guided loss terms to the conventional EDL and a series of uncertainty-guiding training strategies to solve these challenges. |
| Outcome: | The proposed method achieves better OOV/OOD detection performance and generalization ability on OOV entities compared to state-of-the-art methods. |
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach (D19-1)
Copied to clipboard
| Challenge: | Existing methods for text emotion distribution learning require a large amount of training data, which is difficult to obtain due to inconsistent perception of fine-grained emotion intensity. |
| Approach: | They propose a meta-learning approach to learn text emotion distributions from a small sample using tensor decomposition to capture contextual semantic similarity. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a widely used EDL dataset. |
Mahānāma: A Unique Testbed for Literary Entity Discovery and Linking (2025.emnlp-main)
Copied to clipboard
| Challenge: | High lexical variation, ambiguous references, and long-range dependencies make entity resolution in literary texts particularly challenging. |
| Approach: | They present a large-scale dataset for end-to-end Entity Discovery and Linking (EDL) in Sanskrit. |
| Outcome: | The proposed dataset is aligned with an English knowledge base to support cross-lingual linking. |