Scalable Zero-shot Entity Linking with Dense Entity Retrieval

Introduction

problem： Scale is a key challenge for entity linking; there are millions of possible entities to consider for each mention.

previous works： manually curated mention tables；Wikipedia link popularity ； gold Wikipedia entity categories

our work: BERT-based models for large scale entity linking when used in a zero
shot setup, where there is no external knowledge and a short text description provides the only information we have for each entity.present an extensive evaluation of the accuracy-speed tradeoff inherent to large pre-trained models.

details: we introduce a two stage ap proach for zero-shot linking (see Figure 1 for an overview), based on fifine-tuned BERT architectures . In the fifirst stage, we do re trieval in a dense space defifined by a bi-encoder that independently embeds the mention context and the entitydescriptions . Each retrieved candidate is then ex amined more carefully with a cross-encoder that concatenates the mention and entity text, follow ing Logeswaran et al. ( 2019 ). This overall approach is conceptually simple but highly effective, as we show through detailed experiments.

Defifinition and Task Formulation 略