  Local models have recently attained astounding performances in Entity
Disambiguation (ED), with generative and extractive formulations being the most
promising research directions. However, previous works limited their studies to
using, as the textual representation of each candidate, only its Wikipedia
title. Although certainly effective, this strategy presents a few critical
issues, especially when titles are not sufficiently informative or
distinguishable from one another. In this paper, we address this limitation and
investigate to what extent more expressive textual representations can mitigate
it. We thoroughly evaluate our approach against standard benchmarks in ED and
find extractive formulations to be particularly well-suited to these
representations: we report a new state of the art on 2 out of 6 benchmarks we
consider and strongly improve the generalization capability over unseen
patterns. We release our code, data and model checkpoints at
https://github.com/SapienzaNLP/extend.
