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Processing complex and ambiguous named entities is a challenging researchproblem, but it has not received sufficient attention from the natural languageprocessing community. In this short paper, we present our participation in theEnglish track of SemEval-2022 Task 11: Multilingual Complex Named EntityRecognition. Inspired by the recent advances in pretrained Transformer languagemodels, we propose a simple yet effective Transformer-based baseline for thetask. Despite its simplicity, our proposed approach shows competitive resultsin the leaderboard as we ranked 12 over 30 teams. Our system achieved a macroF1 score of 72.50% on the held-out test set. We have also explored a dataaugmentation approach using entity linking. While the approach does not improvethe final performance, we also discuss it in this paper.

LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition