Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information

Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Guoqing Zhao, Xin Wei, Xiaojie Yuan


Abstract
Entity linking, which aims at aligning ambiguous entity mentions to their referent entities in a knowledge base, plays a key role in multiple natural language processing tasks. Recently, zero-shot entity linking task has become a research hotspot, which links mentions to unseen entities to challenge the generalization ability. For this task, the training set and test set are from different domains, and thus entity linking models tend to be overfitting due to the tendency of memorizing the properties of entities that appear frequently in the training set. We argue that general ultra-fine-grained type information can help the linking models to learn contextual commonality and improve their generalization ability to tackle the overfitting problem. However, in the zero-shot entity linking setting, any type information is not available and entities are only identified by textual descriptions. Thus, we first extract the ultra-fine entity type information from the entity textual descriptions. Then, we propose a hierarchical multi-task model to improve the high-level zero-shot entity linking candidate generation task by utilizing the entity typing task as an auxiliary low-level task, which introduces extracted ultra-fine type information into the candidate generation task. Experimental results demonstrate the effectiveness of utilizing the ultra-fine entity type information and our proposed method achieves state-of-the-art performance.
Anthology ID:
2022.coling-1.214
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2429–2437
Language:
URL:
https://aclanthology.org/2022.coling-1.214
DOI:
Bibkey:
Cite (ACL):
Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Guoqing Zhao, Xin Wei, and Xiaojie Yuan. 2022. Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2429–2437, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information (Sui et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.214.pdf
Code
 suixuhui/etzel