Learn to Not Link: Exploring NIL Prediction in Entity Linking

Fangwei Zhu, Jifan Yu, Hailong Jin, Lei Hou, Juanzi Li, Zhifang Sui

Abstract
Entity linking models have achieved significant success via utilizing pretrained language models to capture semantic features. However, the NIL prediction problem, which aims to identify mentions without a corresponding entity in the knowledge base, has received insufficient attention. We categorize mentions linking to NIL into Missing Entity and Non-Entity Phrase, and propose an entity linking dataset NEL that focuses on the NIL prediction problem.NEL takes ambiguous entities as seeds, collects relevant mention context in the Wikipedia corpus, and ensures the presence of mentions linking to NIL by human annotation and entity masking. We conduct a series of experiments with the widely used bi-encoder and cross-encoder entity linking models, results show that both types of NIL mentions in training data have a significant influence on the accuracy of NIL prediction. Our code and dataset can be accessed at https://github.com/solitaryzero/NIL_EL.
Anthology ID:
2023.findings-acl.690
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10846–10860
Language:
URL:
https://aclanthology.org/2023.findings-acl.690
DOI:
10.18653/v1/2023.findings-acl.690
Bibkey:
Cite (ACL):
Fangwei Zhu, Jifan Yu, Hailong Jin, Lei Hou, Juanzi Li, and Zhifang Sui. 2023. Learn to Not Link: Exploring NIL Prediction in Entity Linking. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10846–10860, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Learn to Not Link: Exploring NIL Prediction in Entity Linking (Zhu et al., Findings 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-acl.690.pdf
Video:
 https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-acl.690.mp4
PDF Search Video