Knowledge-Rich Self-Supervised Entity Linking

• URL: http://arxiv.org/abs/2112.07887v1
• Date: Wed, 15 Dec 2021 05:05:12 GMT
• Title: Knowledge-Rich Self-Supervised Entity Linking
• Authors: Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
• Abstract summary: Knowledge-RIch Self-Supervision ($tt KRISSBERT$) is a universal entity linker for four million UMLS entities. Our approach subsumes zero-shot and few-shot methods, and can easily incorporate entity descriptions and gold mention labels if available. Without using any labeled information, our method produces $tt KRISSBERT$, a universal entity linker for four million UMLS entities.
• Score: 58.838404666183656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity linking faces significant challenges, such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck and cannot effectively handle unseen entities. Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia. In this paper, we explore Knowledge-RIch Self-Supervision ($\tt KRISS$) for entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. For inference, it samples self-supervised mentions as prototypes for each entity and conducts linking by mapping the test mention to the most similar prototype. Our approach subsumes zero-shot and few-shot methods, and can easily incorporate entity descriptions and gold mention labels if available. Using biomedicine as a case study, we conducted extensive experiments on seven standard datasets spanning biomedical literature and clinical notes. Without using any labeled information, our method produces $\tt KRISSBERT$, a universal entity linker for four million UMLS entities, which attains new state of the art, outperforming prior self-supervised methods by as much as over 20 absolute points in accuracy.

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