  Existing metonymy resolution approaches rely on features extracted from
external resources like dictionaries and hand-crafted lexical resources. In
this paper, we propose an end-to-end word-level classification approach based
only on BERT, without dependencies on taggers, parsers, curated dictionaries of
place names, or other external resources. We show that our approach achieves
the state-of-the-art on 5 datasets, surpassing conventional BERT models and
benchmarks by a large margin. We also show that our approach generalises well
to unseen data.
