  Entity linking is a prominent thread of research focused on structured data
creation by linking spans of text to an ontology or knowledge source. We
revisit the use of structured prediction for entity linking which classifies
each individual input token as an entity, and aggregates the token predictions.
Our system, called SpEL (Structured prediction for Entity Linking) is a
state-of-the-art entity linking system that uses some new ideas to apply
structured prediction to the task of entity linking including: two refined
fine-tuning steps; a context sensitive prediction aggregation strategy;
reduction of the size of the model's output vocabulary, and; we address a
common problem in entity-linking systems where there is a training vs.
inference tokenization mismatch. Our experiments show that we can outperform
the state-of-the-art on the commonly used AIDA benchmark dataset for entity
linking to Wikipedia. Our method is also very compute efficient in terms of
number of parameters and speed of inference.
