Knowledge-Rich Self-Supervised Entity Linking

12/15/2021
by   Sheng Zhang, et al.
0

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 (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 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.

READ FULL TEXT
research
10/21/2020

Clustering-based Inference for Zero-Shot Biomedical Entity Linking

Due to large number of entities in biomedical knowledge bases, only a sm...
research
09/02/2021

Entity Linking and Discovery via Arborescence-based Supervised Clustering

Previous work has shown promising results in performing entity linking b...
research
06/18/2019

Zero-Shot Entity Linking by Reading Entity Descriptions

We present the zero-shot entity linking task, where mentions must be lin...
research
08/07/2023

Improving Few-shot and Zero-shot Entity Linking with Coarse-to-Fine Lexicon-based Retriever

Few-shot and zero-shot entity linking focus on the tail and emerging ent...
research
06/17/2021

A Self-supervised Method for Entity Alignment

Entity alignment, aiming to identify equivalent entities across differen...
research
07/06/2022

Strong Heuristics for Named Entity Linking

Named entity linking (NEL) in news is a challenging endeavour due to the...
research
04/18/2021

Low-rank Subspaces for Unsupervised Entity Linking

Entity linking is an important problem with many applications. Most prev...

Please sign up or login with your details

Forgot password? Click here to reset