Year: 2020
Type: conference-paper
Abstract: This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off.We present a two-stage zero-shot linking algorithm, where each entity is defined only by a short textual description.The first stage does retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions.Each candidate is then re-ranked with a crossencoder, that concatenates the mention and entity text.Experiments demonstrate that this approach is state of the art on recent zeroshot benchmarks (6 point absolute gains) and also on more established non-zero-shot evaluations (e.g.TACKBP-2010), despite its relative simplicity (e.g.no explicit entity embeddings or manually engineered mention tables).We also show that bi-encoder linking is very fast with nearest neighbour search (e.g.linking with 5.9 million candidates in 2 milliseconds), and that much of the accuracy gain from the more expensive crossencoder can be transferred to the bi-encoder via knowledge distillation.Our code and models are available at https://github. com/facebookresearch/BLINK. wikipedia dense spaceMy kids really enjoyed a ride in the Jaguar!Jaguar is the luxury vehicle brand. Jaguar_carsJaguar! is a junior roller coaster.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3104748221