  Processing complex and ambiguous named entities is a challenging research
problem, but it has not received sufficient attention from the natural language
processing community. In this short paper, we present our participation in the
English track of SemEval-2022 Task 11: Multilingual Complex Named Entity
Recognition. Inspired by the recent advances in pretrained Transformer language
models, we propose a simple yet effective Transformer-based baseline for the
task. Despite its simplicity, our proposed approach shows competitive results
in the leaderboard as we ranked 12 over 30 teams. Our system achieved a macro
F1 score of 72.50% on the held-out test set. We have also explored a data
augmentation approach using entity linking. While the approach does not improve
the final performance, we also discuss it in this paper.
